G22.2110 Programming Languages
2009-10 Fall
Allan Gottlieb
Wednesday 5-6:50pm Room 109 Ciww

Start Lecture #1

Chapter 0: Administrivia

I start at 0 so that when we get to chapter 1, the numbering will agree with the text.

0.1: Contact Information

0.2: Course Web Page

There is a web site for the course. You can find it from my home page, which is http://cs.nyu.edu/~gottlieb

0.3: Textbook

The course text is Scott, "Programming Language Pragmatics", Third Edition (3e).

0.4: Computer Accounts and Mailman Mailing List

0.5: Grades

Grades are based on the labs and the final exam, with each very important. The weighting will be approximately
30%*LabAverage + 30%*MidTermExam + 40%*FinalExam (but see homeworks below).

0.6: The Upper Left Board

I use the upper left board for lab/homework assignments and announcements. I should never erase that board. Viewed as a file it is group readable (the group is those in the room), appendable by just me, and (re-)writable by no one. If you see me start to erase an announcement, let me know.

I try very hard to remember to write all announcements on the upper left board and I am normally successful. If, during class, you see that I have forgotten to record something, please let me know. HOWEVER, if I forgot and no one reminds me, the assignment has still been given.

0.7: Homeworks and Labs

I make a distinction between homeworks and labs.

Labs are

Homeworks are

0.7.1: Homework Numbering

Homeworks are numbered by the class in which they are assigned. So any homework given today is homework #1. Even if I do not give homework today, the homework assigned next class will be homework #2. Unless I explicitly state otherwise, all homeworks assignments can be found in the class notes. So the homework present in the notes for lecture #n is homework #n (even if I inadvertently forgot to write it to the upper left board).

0.7.2: Doing Labs on non-NYU Systems

You may solve lab assignments on any system you wish, but ...

0.7.3: Obtaining Help with the Labs

Good methods for obtaining help include

  1. Asking me during office hours (see web page for my hours).
  2. Asking the mailing list.
  3. Asking another student, but ...
    Your lab must be your own.
    That is, each student must submit a unique lab. Naturally, simply changing comments, variable names, etc. does not produce a unique lab.

0.8: A Grade of Incomplete

The new rules set by GSAS state:

  3.6.  Incomplete Grades: An unresolved grade, I, reverts to F one
        year after the beginning of the semester in which the course
        was taken unless an extension of the incomplete grade has been
        approved by the Vice Dean.
     3.6.1.  At the request of the departmental DGS and with the
             approval of the course instructor, the Vice Dean will
             review requests for an extension of an incomplete grade.
     3.6.2.  A request for an extension of incomplete must be
             submitted before the end of one year from the beginning
             of the semester in which the course was taken.
     3.6.3.  An extension of an incomplete grade may be requested for
             a period of up to, but not exceeding, one year
     3.6.4.  Only one one-year extension of an incomplete may be granted.
     3.6.5.  If a student is approved for a leave of absence (See 4.4)
             any time the student spends on that leave of absence will
             not count toward the time allowed for completion of the

0.9 Academic Integrity Policy

This email from the assistant director, describes the policy.

    Dear faculty,

    The vast majority of our students comply with the
    department's academic integrity policies; see


    Unfortunately, every semester we discover incidents in
    which students copy programming assignments from those of
    other students, making minor modifications so that the
    submitted programs are extremely similar but not identical.

    To help in identifying inappropriate similarities, we
    suggest that you and your TAs consider using Moss, a
    system that automatically determines similarities between
    programs in several languages, including C, C++, and Java.
    For more information about Moss, see:


    Feel free to tell your students in advance that you will be
    using this software or any other system.  And please emphasize,
    preferably in class, the importance of academic integrity.

    Rosemary Amico
    Assistant Director, Computer Science
    Courant Institute of Mathematical Sciences

Chapter 1: Introduction

Brief History

Very early systems were programmed in machine language.

Next came assembly language where the programmer uses mnemonics for the opcode, but still writes one line for every machine instruction. Simple translators, called assemblers, translate this language into machine code. Assembly language, like machine language is completely non-portable. Later assemblers supported macros, which helped the tedium, but did not increase portability.

Today high-level languages dominate and are translated by sophisticated software called compilers. The 3e has much material on compilers, but I will de-emphasize that portion since we give a course on the subject (G22-2130). My extensive lecture notes for 2130 can be found off my home page.

1.1: The Art of Language Design

What is a Program?

At the most primitive level, a program is a sequence of characters chosen from an alphabet. But not all sequences are legal: there are syntactic restrictions (e.g., identifiers in C cannot begin with a digit). We also ascribe meaning to programs using the semantics of the language, which further restricts the legal sequences (e.g., declarations must precede uses in Ada).

A Programming Language specifies what character sequences are legal and what these sequences mean (i.e., it specifies the syntax and semantics).

Why So Many Languages?

Desirable Traits

Why are some languages so much more popular/successful than others?

1.2: The Programming Language Spectrum

Imperative vs. Declarative Languages

An imperative language programmer essentially tells the computer exactly how to solve the problem at hand. In contrast a declarative language is higher-level: The programmer describes only what is to be done. For example, malloc/free (especially free) is imperative, but garbage collection is normally found in declarative languages. The Prolog example below illustrates the declarative style.

There are broad spectrum languages like Ada that try to provide both low-level and high-level features. By necessity, such languages are large and complex.

von Neumann: Fortran, Pascal, C, Ada 83

This is the most common programming paradigm and largely subsumes the Object-Oriented class described next. It is the prototypical imperative language style.

The defining characteristic is that the state of a program (very roughly the values of the variables) changes during execution. Very often the change is the result of executing an assignment statement.

Recently, the term von Neumann is used to refer to serial execution of a program (i.e., no concurrency).

Object-Oriented: Simula 67, Smalltalk, Ada 95, Java, C#, C++

Languages that emphasize information hiding and (especially) inheritance. Data structures are bundled together with the operators using them. Most are von Neumann, but pure object-oriented languages like Smalltalk have a different viewpoint of computation, which is viewed as occurring at the objects themselves.

Functional (a.k.a. Applicative): Scheme, ML, Haskell

These languages are based on the lambda calculus. They emphasize functions (without side effects) and discourage assignment statements and other forms of mutable state. Functions are first-class objects; new functions can be constructed while the program is running. This will be emphasized when we study Scheme. Here is a taste.

(define sumtwo
  (lambda (A B)
    (+ A B)))

> (sumtwo 5 8)

(define sumthree
  (lambda (A B C)
    (+ (+ A B) C)))

> (sumthree 5 8 2)

(define oosumtwo              ; "object oriented" sum
  (lambda (A B)
    ( (lambda (X) (+ X B))    ; this is the anonymous "add B" function
      A)))                    ; which we apply to A

> (oosumtwo 5 8)              ; the anonymous "add 8" temporarily existed

Logic (Declarative, Constraint Based): Prolog

A program is a set of constraints and rules. The following example is a fixed version of Figure 1.14.

    gcd(A,B,G) :- A=B, G=A.
    gcd(A,B,G) :- A>B, C is A-B, gcd(C,B,G)
    gcd(A,B,G) :- B>A, C is B-A, gcd(C,A,G)

Homework: 1.4. Unless otherwise stated numbered homework problems are from the Exercises section of the current chapter. For example this problem can be found in section 1.8.

Scripting (Shell, Perl, Python)

Often used as glue to connect other programs together.


Many languages contain aspects of several of these classes. As mentioned C++, Java, and C# are both von Neumann and object-oriented. The language OHaskell is object-oriented and functional.


Concurrency is not normally considered a separate class. It is usually obtained via extensions to languages in the above classes. These extensions can be in the form of libraries. A few languages have concurrency features, but the bulk of the language is devoted to serial execution. For example, threads in Java and rendezvous in Ada.

(High-Level vs Low-Level) High-Level Languages

In this course we are considering only high-level languages, i.e., we exclude machine and assembly languages. However, the class of high-level languages is quite broad and some are considered higher level than others.

Thus when comparing languages, we often call C and Fortran low-level since the programmer has more control and must expend more effort. In contrast, languages like Scheme and Haskell are considered high-level. They require less effort for a given task but give the programmer less control and the run-time performance is not as easy to predict. Perhaps we should call C a low-level, high-level language, but that suggestion is too awkward to take seriously.

There are wide spectrum languages like Ada and C++ that provide both low-level control when desired, in addition to high-level features such as garbage-collection and array manipulation. The cost of this is a large, complex language.

Homework: Page 16 CYU (check your understanding) #2.

Characteristics of Modern Languages

Modern general-purpose languages such as Ada, C++, and Java share many characteristics.

1.3: Why Study Programming Languages

Become Familiar with Various Idioms

Learning several languages exposes you to multiple techniques for problem solving, some of which are idioms in the various languages. Your exposure to the different idioms enlarges your toolbox and increases your programming power, even if you only use a very few, closely related, languages.

Elegance, Beauty and Coolness

The automatic pattern matching and rule application of Prolog, once understood, is real neat. It is certainly not something I would have thought of had I never seen Prolog. If you encounter problems for which Prolog is well suited and for which a Prolog based solution is permitted, the programming effort saved is substantial. A very primitive form of the Prolog unification is the automatic dependency checking, topological sorting, and evaluation performed by spreadsheets.

I have never written a serious program using continuation passing, but nonetheless appreciate the elegance and (mathematical-like) beauty the technique offers. I hope you will too when we study Scheme.

Applying Programming Language / Compiler Techniques outside the Domain

Very few of you will write a compiler or be part of a programming language design effort. Nonetheless, the techniques used in these areas can be applied to many other problems. For example, lexing and especially parsing can be used to produce a front end for many systems. Brian Kernighan refers to them as little languages; others call them domain-specific languages or application-specific languages.

1.4: Compilation and Interpretation

The standard reference for compilers is the Dragon Book now in its third incarnation (gaining an author with each new life). My notes for our compiler course using this text can be found off my home page (click on previous courses).

A Compiler is a translator from one language, the input or source language, to another language, the output or target language.

Often, but not always, the target language is an assembler language or the machine language for a computer processor.

Note that using a compiler requires a two step process to run a program.

  1. Execute the compiler (and possibly an assembler) to translate the source program into a machine language program.
  2. Execute the resulting machine language program, supplying appropriate input.

This should be compared with an interpreter, which accepts the source language program and the appropriate input, and itself produces the program output.

Sometimes both compilation and interpretation are used. For example, consider typical Java implementations. The (Java) source code is translated (i.e., compiled) into bytecodes, the machine language for an idealized virtual machine, the Java Virtual Machine or JVM. Then an interpreter of the JVM (itself normally called a JVM) accepts the bytecodes and the appropriate input, and produces the output. This technique was quite popular in academia some time ago with the Pascal programming language and P-code.

1.5: Programming Environments

The Compilation Tool Chain

This section of 3e is quite clear, but rather abbreviated. It is enough for this class, but if you are interested in further details you can look here at the corresponding section from the compilers course (G22.2130).

Other Parts of the Programming Environment

In addition to the compilation tool chain, the programming environment includes such items as pretty printers, debuggers, and configuration managers. These aids may be standalone or integrated in an IDE or sophisticated editing system like emacs.

1.6: An Overview of Compilation

The material in 1.6 of the 3e, including the subsections below, is briefly covered in section 1.2 of my compilers notes. The later chapters of those notes naturally cover the material in great depth. However, we will not be emphasizing compilation aspects of programming languages in this course so will be content with the following abbreviated coverage.

Homework: 1.1 (a-d).

1.6.1: Lexical and Syntax Analysis

1.6.2: Semantic Analysis and Intermediate Code Generation

1.6.3: Target Code Generation

1.6.4: Code Improvement

1.7: Summary


Every chapter ends with these same four sections. I will not be repeating them in the notes. You should always read the summary.

1.8: Exercises

1.9: Explorations

1.10: Bibliographic Notes

Chapter 2: Programming Language Syntax

We cover this material (in much more depth) in the compilers course. For the programming language course we are content with the very brief treatment in the previous chapter.

Homework: 2.1(a), 2.3, 2.9(a,b)

Start Lecture #2

Chapter 3: Names, Scopes, and Bindings

Advantages of High-level Programming Languages

They are Machine Independent.

This is clear.

They are easier to use and understand.

This is clearly true but the exact reasons are not clear. Many studies have shown that the number ofbugs per line of code is roughly constant between low- and high-level languages. Since low-level languages need more lines of code for the same functionality, writing a programs using these languages results in more bugs.

Studies also support the statement that programs can be written quicker in high-level languages (comparable number of documented lines of code per day for high- and low-level languages).

What is not clear is exactly what aspects of high-level languages are the most important and hence how should such languages be designed. There has been some convergence, which will be reflected in the course, but this is not a dead issue.


A name is an identifier, i.e., a string of characters (with some restrictions) that represents something else.

Many different kinds of things can be named, for example.

Names are an important part of abstraction.

3.1: The Notion of Binding Time

In general a binding is as association of two things. We will be interesting in binding a name to the thing it names and will see that a key question is when does this binding occur. The answer to that question is called the binding time.

There is quite a range of possibilities. The following list is ordered from early binding to late binding.

Static binding refers to binding performed prior to run time. Dynamic binding refers to binding performed during run time. These terms are not very precise since there are many times prior to run time, and run time itself covers several times.

Trade-offs for Early vs. Late Binding

Run time is generally reduced if we can compile, rather than interpret, the program. It is typically further reduced if the compiler can make more decisions rather than deferring them to run time, i.e., if static binding can be used.

Summary: The earlier (binding time) decisions are made, the better the code that the compiler can produce.

Early binding times are typically associated with compiled languages while late binding times are typically associated with interpreted languages.

The compiler is easier to implement if there are bindings done even earlier than compile time

We shall see, however, that dynamic binding gives added flexibility. For one example, late-binding languageslike Smalltalk, APL, and most scripting languages permit polymorphism: The same code can be executed on objects of different types.

Moreover, late-binding gives control to the programmer since they control run time. This gives increased flexibility when compared to early-binding, which is done by the compiler or language designer.

3.2: Object Lifetime and Storage Management


We use the term lifetime to refer to the interval between creation and destruction.

For example, the interval between the binding's creation and destruction is the binding's lifetime. For another example, the interval between the creation and destruction of an object is the object's lifetime.

How can the binding lifetime differ from the object lifetime?

Storage Allocation Mechanisms

There are three primary mechanisms used for storage allocation:

  1. Static objects maintain the same (virtual) address throughout program execution.
  2. Stack objects are allocated and deallocated in a last-in, first-out (LIFO, or stack-like) order. The allocations/deallocations are normally associated with procedure or block entry/exit.
  3. Heap objects are allocated/deallocated at arbitrary times. The price for this flexibility is that the memory management operations are more expensive.

We study these three in turn.

3.2.1: Static Allocation

This is the simplest and least flexible of the allocation mechanisms. It is designed for objects whose lifetime is the entire program execution.

The obvious examples are global variables. These variables are bound once at the beginning and remain bound until execution ends; that is their object and binding lifetimes are the entire execution.

Static binding permits slightly better code to be compiled (for some architectures and compilers) since the addresses are computable at compile time.

Using Static Allocation for all Objects

In a (perhaps overzealous) attempt to achieve excellent run time performance, early versions of the Fortran language were designed to permit static allocation of all objects.

The price of this decision was high.

Before condemning this decision, one must remember that, at the time Fortran was introduced (mid 1950s), it was believed by many to be impossible for a compiler to turn out high-quality machine code. The great achievement of Fortran was to provide the first significant counterexample to this mistaken belief.

Local Variables

For languages supporting recursion (which includes recent versions of Fortran), the same local variable name can correspond to multiple objects corresponding to the multiple instantiations of the recursive procedure containing the variable. Thus a static implementation is not feasible and stack-based allocation is used instead. These same considerations apply to compiler-generated temporaries, parameters, and the return value of a function.


If a constant is constant throughout execution (what??, see below), then it can be stored statically, even if used recursively or by many different procedures. These constants are often called manifest constants or compile time constants.

In some languages a constant is just an object whose value doesn't change (but whose lifetime can be short). In ada

        v : integer;
        get(v);                     -- input a value for v
          c : constant integer := v;  -- c is a "constant"
          v := 5;                   -- legal; c unchanged.
          c := 5;                   -- illegal
    end loop;
For these constants static allocation is again not feasible.

3.2.2: Stack-Based Allocation

This mechanism is tailored for objects whose lifetime is the same as the block/procedure in which it is declared. Examples include local variables, parameters, temporaries, and return values. The key observation is that the lifetimes of such objects obey a LIFO (stack-like) discipline:
If object A is created prior to object B, then A will be destroyed no earlier than B .

When procedure P is invoked the local variables, etc for P are allocated together and are pushed on a stack. This stack is often called the control stack and the data pushed on the stack for a single invocation of a procedure/block is called the activation record or frame of the invocation.

When P calls Q, the frame for Q is pushed on to the stack, right after the frame for P and the LIFO lifetime property guarantees that we will remove frames from the stack in the safe order (i.e., will always remove (pop) the frame on the top of the stack.

This scheme is very effective, but remember it is only for objects with LIFO lifetimes. For more information, see any compiler book or my compiler notes.

3.2.3: Heap-Based Allocation

What if we don't have LIFO lifetimes and thus cannot use stack-based allocation methods? Then we use heap-based allocation, which just means we can allocate and destroy objects in any order and with lifetimes unrelated to program/block entry and exit.

A heap is a region of memory from which allocations and destructions can be performed at arbitrary times.

Remark: Please do not confuse these heaps with the heaps you may have learned in a data structures course. Those (data-structure) heaps are used to implement priority queues; they are not used to implement our heaps.

What objects are heap allocated?

Implementing Heaps

The question is how do respond to allocate/destroy commands? Looked at from the memory allocators viewpoint, the question is how to implement requests and returns of memory blocks (typically, the block returned must be one of those obtained by a request, not just a portion of a requested block).

Note that, since blocks are not necessarily returned in LIFO order, the heap will have not simply be a region of allocated memory and another region of available memory. Instead it will have free regions interleaved with allocated regions.

So the first question becomes, when a request arrives, which region should be (partially) uses to satisfy it. Each algorithmic solution to this question (e.g., first fit, best fit, worst fit, circular first fit, quick fit, buddy, Fibonacci) also includes a corresponding algorithm for processing the return of a block.

What happens when the user no longer needs the heap-allocated space?

Poorly done manual deallocation is a common programming error.

We can run out of heap space for at least three different reasons.

  1. What if there is not enough free space in total for the new request and all the currently allocated space is needed?
    Solution: Abort.
  2. What if we have several, non-contiguous free blocks, none of which are big enough for the new request, but in total they are big enough?
    This is called external fragmentation since the wasted space is outside (external to) all allocated blocks.
    Solution: Compactify.
  3. What if some of the allocated blocks are no longer accessible by the user, but haven't been returned?
    Solution: Garbage Collection (next section).

3.2.4: Garbage Collection

The 3e covers garbage collection twice. It is covered briefly here in 3.2.4 and in more depth in 7.7.3. My coverage here contains much of 7.7.3.

A garbage collection algorithm is one that automatically deallocates heap storage when it is no longer needed.

It should be compared to manual deallocation functions such as free(). There are two aspects to garbage collection: first, determining automatically what portions of heap allocated storage will (definitely) not be used in the future, and second making this unneeded storage available for reuse.

After describing the pros and cons of garbage collection, we describe several of the algorithms used.

Advantages and Disadvantages of Garbage Collection

We start with the negative. Providing automatic reclamation of unneeded storage is an extra burden for the language implementer.

More significantly, when the garbage collector is running, machine resources are being consumed. For some programs the garbage collection overhead can be a significant portion of the total execution time. If, as is often the case, the programmer can easily tell when the storage is no longer needed, it is normally much quicker for the programmer to free it manually than to have a garbage collector do it.

Homework: What would characterizes programs for which garbage collection causes significant overhead?

The advantages of garbage collection are quite significant (perhaps they should be considered problems with manual deallocation). Unfortunately, there are often times when it seems obvious that storage is no longer needed; but it fact it used later. The mistaken use of previously freed storage is called a dangling reference. One possible cause is that the program is changed months later and a new use is added.

Another problem with manual deallocation is that the user may forget to do it. This bug, called a storage leak might only turn up in production use when the program is run for an extended period. That is if the program leaks 1KB/sec, you might well not notice any problem during test runs of 5 minutes, but a production run may crash (or begin to run intolerably slowly) after a month.

The balance is swinging in favor of garbage collection.

Reference Counting

This is perhaps the simplest scheme, but misses some of the garbage.


  1. Assume L is the only pointer to a circular list and we assign a new value to L. The circular list now cannot be accessed, but every reference count is 1 so nothing will be freed. Storage has leaked.
  2. C programmers can fairly easily implement the reference counting algorithm for for their heap storage using the above algorithm. However, in C it is hard to tell when new pointers are being created because the type system is loose and pointers are really just integers.

Tracing Collectors

The idea is to find the objects that are live and then reclaim all dead objects.

A heap object is live if it can be reached starting from a non-heap object and following pointers. The remaining heap objects are dead. That is, we start at machine registers, stack variables and constants, and static variables and constants that point to heap objects. These starting places are called roots. It is assumed that pointers to heap objects can be distinguished from other objects (e.g., integers).

The idea is that for each root we preform a graph traversal following all pointers. Everything we find this way is live; the rest is dead.


This is a two phase algorithm as the name suggests and basically follows the idea just given: We mark live objects during the mark phase and reclaim dead ones during the sweep phase.

It is assumed that each object has an extra mark bit. The code below defines a procedure mark(p), which uses the mark bit. Please don't confuse the uses of the name mark as both a procedure and a bit.

    Procedure GC is
       for each root pointer p
       for each root pointer p
           p.mark := false
    Procedure mark(p) is
       if not p.mark             -- i.e. if the mark bit is not set
          p.mark := true
          for each pointer p.x   -- i.e. each ptr in obj pointed to by p
             mark(p.x)           -- i.e. invoke the mark procedure on x recursively
    Procedure sweep is
       for each object x in the heap
          if not x.mark
             x.mark := false

stop and copy

Copying (a.k.a. Stop-and-Copy)

A performance problem with mark-and-sweep is that it moves each dead object (i.e., each piece of garbage). Since experimental data from LISP indicates that, when garbage collection is invoked, about 2/3 of the heap is garbage, it would be better to leave the garbage alone and move the live data instead. That is the motivation for stop-and-copy.

Divide the heap into two equal size pieces, often called FROM and TO. Initially, all allocations are performed using FROM; TO is unused. When FROM is (nearly) full, garbage collection is performed. During the collection, all the live data is moved from FROM to TO. Naturally the pointers must now point to TO. The tricky part is that live data may move while there are still pointers to it. For this reason a forwarding address is left in FROM. Once, all the data is moved, we flip the names FROM and TO and resume program execution.

    Procedure GC is
       for each root pointer p
          p := traverse(p)
    Procedure traverse is
       if p.ALL is a forwarding address
          p := the forwarding address in p.ALL
          return p
          newP := copy(p,TO)
          p.ALL := newP         -- write forwarding address
          for each pointers x in newP.ALL
             newP.x := traverse(newP.x)
          return newP

The movie on the right illustrates stop and copy in action.

  1. The top frame of the movie is the initial state. There are two root pointers p and q, and three heap objects. Initially p points to the first object, q points to the second. The second object contains pointers to the other two.
  2. In the second frame we see the state after traverse has been called with argument p and the assignment statement in GC has been executed. Note that traverse(p) executes the else arm of the if. The object has been copied and the forwarding pointer set. In this diagram I assumed that the forwarding pointer is the same size as the original object. It is required that an object is at least as large as a pointer. The previous contents of the object have been overwritten by this forwarding pointer, but no information is lost since the copy (in TO space) contains the original data. There are no internal pointers so the for loop is empty. When traverse completes, p is set to point to the new object.
  3. The third frame shows the state while traverse(q) is in progress. The first two statements of the else branch have been executed. The middle object has been copied and the forwarding pointer has been set. This forwarding pointer will never be used since there are no other pointers to q.ALL. Note that the two internal pointers refer to the old (FROM-space) versions of the first and third objects. Indeed, those pointers might have been overwritten by the forwarding pointer, but again no information is lost since the TO-space copy has the original values.
  4. The last frame shows the final state. A great deal has happened since the previous frame. The traverse(q) execution continues reaches the for loop. This time there are two pointers in the copied block so traverse will be called recursively two times. The first pointer refers to an already-moved block so the then branch is taken and, when traverse returns, the pointer is updated. The second pointer points to the original version of the third block so the else branch is taken. As in the top frame, the block is copied and the forwarding pointer is set. Again, when traverse returns, the pointer in the to-space copy of the second block is updated. Finally, traverse(q) returns and q is updated. We are done. The FROM space is superfluous, TO space is now what FROM space was at the start.


  1. Stop-and-copy compactifies with no additional code. Specifically, the copies into TO space are contiguous so when garbage collection ends, there is no external fragmentation.
  2. With mark-and-sweep, the garbage is moved, but the live objects are not. Thus, when collection ends, we have the in-use and free blocks intersperses, i.e., external fragmentation. Extra effort is needed to compactify.
  3. It has been observed that most garbage is fresh, i.e., newly created. Generational collectors separate out in-use blocks that have survived collections into a separate region, which is garbage collected less frequently.
  4. As described above, both mark-and-sweep and stop-and-copy assume that no activity occurs during a pass of the collector. With multiprocessors (e.g., multicore CPUs) it is intriguing to consider have the collector run simultaneous with the user program (the so-called mu tater)

    procedure f is
       x : integer;
       x := 4;
          x : float;
          x := 21.75;
    end f;

Homework: CYU p. 121 (2, 4, 9)

Homework: Give a program in C that would not work if local variables were statically.

3.3: Scope Rules

The region of program text where a binding is active is called the scope of the binding.

Note that this can be different from the lifetime. The lifetime of the outer x in the example on the right is all of procedure f, but the scope of that x has a hole where the inner x hides the binding. We shall see below that in some languages, the hole can be filled.

Static vs Dynamic Scoping

  procedure main is
     x : integer := 1;
     procedure f is
     end f;
     procedure g is
        x : integer := 2;
     end g;
  end main;

Before we begin in earnest, I thought a short example might be helpful. What is printed when the procedure main on the right is run?

That looks pretty easy, main just calls g, g just calls f, and f just prints (put is ada-speak for print) x. So x is printed.

Sure, but which x? There are, after all, two of them. Is is ambiguous, i.e., erroneous?

Since this section about scope, we see that the question is which x is in scope at the put(x) call? Is it the one declared in main, inside of which f is defined, or is the one inside g, which calls f, or is it both, or neither?

For some languages, the answer is the x declared in main and for others it is the x declared in g. The program is actually written in Ada, which is statically scoped (a.k.a. lexically scoped) and thus gives the answer 1.

How about Scheme, a dialect of lisp?

    (define x 1)
    (define f (lambda () x))
    (define g (lambda () (let ((x 2)) (f))))
We get the same result: when g is evaluated, 1 is printed. Scheme, like, ada, C, Java, C++, C#, ... is statically scoped.

Is every language statically scoped? No, some dialects of Lisp are dynamically scoped, as is Snobol, Tex, and APL. In Perl the programmer gets to choose.

    (setq x 1)
    (defun f () x)
    (defun g () (let ((x 2)) (f)))
In particular, the last program on the right, which is written in emacs lisp, gives 2 when g is evaluated. The two Lisps are actually more similar that they might appear on the right: The emacs Lisp defun (which stands for "define function") is essentially a combination of Scheme's define and lambda.

3.3.1: Static Scoping

In static scoping, the binding of a name can be determined by reading the program text; it does not depend on the execution. Thus it can be determined at compile time.

The simplest situation is the one in early versions of Basic: There is only one scope, the whole program. Recall, that early basic was intended for tiny programs. I believe variable names were limited to two characters, a letter optionally follow by a digit. For large programs a more flexible approach is needed, as given in the next section.

3.3.2: Nested Subroutines, i.e., Nested Scopes

The typical situation is that the relevant binding for a name is the one that occurs in the smallest containing block and the scope of the binding is that block.

So the rule for finding the relevant binding is to look in the current block (I consider a procedure/function definition to be a block). If the name is found, that is the binding. If the name is not found, look in the immediately enclosing scope and repeat until the name is found. If the name is not found at all, the program is erroneous.

What about built in names such as type names (Integer, etc), standard functions (sin, cos, etc), or I/O routines (Print, etc)? It is easiest to consider these as defined in an invisible scope enclosing the entire program.

Given a binding B in scope S, the above rules can be summarized by the following two statements.

  1. B is available in scopes nested inside S, unless B is overridden, in which case it is hidden.
  2. B is not available in scopes enclosing S.

Some languages have facilities that enable the programmer to reference bindings that would otherwise be hidden by statement 1 above. For example

  procedure outer is         procedure outer is
     x : integer := 6;           x : integer := 6;
     procedure inner is          procedure inner is
     begin                          x : integer := 88;
        put(x);                  begin
     end inner;                     put(x,outer.x);
  begin                          end inner;
    inner;                   begin
  end outer;                     inner;
                               end outer2;

Access to Nonlocal Objects

Consider first two easy cases of nested scopes illustrated on the right with procedures outer and inner. How does the left execution of inner find the binding to x? How does the right execution of inner find both bindings of x? We need a link from the activation record of the inner to the activation record of outer. This is called the static link or the access link.

But it is actually more difficult than that. The static link must point to the most recent invocation of the surrounding subroutine.

Of course the nesting can be deeper; but that just means following a series of static links from one scope to the next outer one. Moreover, finding the most recent invocation of outer is not trivial; for example, inner may have been called by a routine nested inside inner. For the details see a compilers book or my course notes.

3.3.3: Declaration Order

There are several questions here.

  1. Must all declarations for a block precede all other statements?
    In Ada the answer is yes. Indeed, the declarations are set off by the syntax.
            procedure <declarations> begin <statements> end;
            declare <declarations> begin <statements> end;
    In C, C++, and java the answer is no. The following is legal.
            int z;   z=4;   int y;
  2. Can one declaration refer to a previous declaration in the same block?
    Yes for Ada, C, C++, Java.
            int z=4;   int y=z;
  3. Do declarations take affect where the block begins (i.e., they apply to the entire block except for holes due to inner scopes) or do they start only at the declaration itself?
            int y;  y=z;   int z=4;
    In Java, Ada, C, C++ they start at the declaration so the example above is illegal. In JavaScript and Modula3 they start at the beginning of the block so the above is legal. In Pascal the declaration starts at block beginning, but can't be used before it is declared. This has a weird effect: In inner declaration hides an outer declaration but can't be used in earlier statements of the inner.


Scheme uses let, let* and letrec to introduce a nested scope. The variables named are given initial values. In the simple case where the initial values are manifest constants, the three let's are the same.

    (let ( (x 2)(y 3) ) body)  ; eval body using new variables x=2 & y=3
We will study the three when we do scheme. For now, I just add that for letrec the scope of each declaration is the whole block (including the preceding declarations, so x and y can depend on each other), for let* the scope starts at the declaration (so y can depend on x, but not the reverse) and for let the declarations start at the body (so neither y nor x can depend on the other). The above is somewhat oversimplified.

Homework: 5, 6(a).

Declarations and Definitions

Many languages (e.g., C, C++, Ada) require names to be declared before they are used, which causes problems for recursive definitions. Consider a payroll program with employees and managers.

    procedure payroll is
      type Employee;
      type Manager is record
         Salary :    Float;
         Secretary : access Employee;   --  access is ada-speak for pointer
      end record;
      type Employee is record
         Salary : Float;
         Boss :   access Manager;
      end record;
    end payroll;

These languages introduce a distinction between a declaration, which simply introduces the name and indicates its scope, and a definition, which fully describes the object to which the name is bound.

Nested Blocks

Essentially all the points made above about nested procedures applies to nested blocks as well. For example the code on the right, using nested blocks, exhibits the same hole in the scope as does the code on the left, using nested procedures.

    procedure outer is      declare
      x : integer;            x : integer;
      procedure inner is    begin
        x : float:            -- start body of outer x is integer
      begin                   declare
        -- body of inner        x : float;
      end inner;              begin
    begin                       -- body of inner, x is float
      -- body of outer        end;
    end outer;                -- more of body of outer. x again integer


Some (mostly interpreted) languages permit a redeclaration in which a new binding is created for a name already bound in the scope. Does this new binding start at the beginning of the scope or just where the redeclaration is written?

    int f (int x) { return x+10; }
    int g (int x) { return f(x); }
    int f (int x) { return x+20; }
Consider the code on the right. The evaluation of g(0) uses the first definition of f and returns 10. Does the evaluation of g(5) use the first f, the one in effect when g was defined, or does it use the second f, the one in effect when g(5) was invoked.

The answer is: it depends. For most languages supporting redeclaration, the second f is used; but in ML it is the first. In other words for most languages the redeclaration replaces the old in all contexts; in ML it replaces the old only in later uses, not previous ones. This has some of the flavor of static vs. dynamic scoping.

3.3.4: Modules

Will be done later.

3.3.5: Module Types and Classes

Will be done later.

3.3.6: Dynamic Scoping

We covered dynamic scoping already.

3.4: Implementing Scope

3.5: The Meaning of Names within a Scope

3.6: The Binding of Referencing Environments

3.7: Macro Expansion

This section will be covered later.

3.8: Separate Compilation

Chapter 4: Semantic Analysis

Chapter 5: Target Machine Architecture

Start Lecture #3

Chapter 6: Control Flow

Remark: We will have a midterm. It will be 1 hour, during recitation. It will not be either next monday or the monday after.

6.1: Expression Evaluation

Most languages use infix notation for built in operators. Scheme is an exception (+ 2 (/ 9 x)).

    function "*" (A, B: Vector) return Float is
      Result: Float :=0.0;
      for I in A'Range loop
        Result := Result + A(I)*B(I);
      end loop;
      return Result;
    end "*";

Some languages permit the programmer to extend the infix operators. An example from Ada is at right. This example assumes Vector has already been declared as a one-dimensional array of floats. A'R gives the range of the legal subscripts, i.e. the bounds of the array. With loops like this it is not possible to access the array out of bounds.

6.1.1: Precedence and Associativity

Most languages agree on associativity, but APL is strictly right to left (no precedence, either). Normal is for most binary operators to be left associative, but exponentiation is right associative (also true in math). Why?

Languages differ considerably in precedence (see figure 6.1 in 3e). The safest rule is to use parentheses unless certain.

Homework: 1. We noted in Section 6.1.1 that most binary arithmetic operators are left-associative in most programming languages. In Section 6.1.4, however, we also noted that most compilers are free to evaluate the operands of a binary operator. Are these statements contradictory? Why or why not?

6.1.2: Assignments

Assignment statements are the dominant example of side effects, where execution does more that compute values. Side effects change the behavior of subsequent statements and expressions.

References and Values

There is a distinction between the container for a value (the memory location) and the value itself. Some values, e.g., 3+4, do not have corresponding containers and, among other things, cannot appear on the left hand side of an assignment. Otherwise said 3+4 is a legal r-value, but not a legal l-value. Given an l-value, the corresponding r-value can be obtained by dereferencing. In most languages, this dereferencing is automatic. Consider

    a := a + 1;
the first a gives the l-value; the second the r-value.


    a := if b < c then d else c;
    a := begin f(b); g(c) end;


The goal of orthogonality of features is to permit the features in any combination possible. Algol 68 emphasized orthogonality. Since it was an expression-oriented language, expressions could appear almost anywhere. There was no separate notion of statement. An example appears on the right. I have heard and read that this insistence on orthogonality lead to significant implementation difficulties.

Combination Assignment Operators

I suspect you all know the C syntax

    a += 4;
It is no doubt often convenient, but its major significance is that in statements like
    a[f(i)] += 4;
it guarantees that f(i) is evaluated only once. The importance of this guarantee becomes apparent if f(i) has side effects, e.g., if f(i) modifies i or prints.

Multiway Assignment (and Tuples)

Some languages, e.g., Perl and Python, permit tuples to be assigned and returned by functions, giving rise to code such as

    a, b  :=  b, a    -- swapping w/o an explicit temporary
    x, y  :=  f(5);   -- this f takes one argument and returns two results

6.1.3: Initialization

Avoids the problem where an uninitialized variable has different values in different runs causing non-reproducible results. Some systems provide default initialization, for example C initializes external variables to zero.

To initialize aggregates requires a way to specify aggregate constants.

Dynamic Checks

An alternative to have the system check during execution if a variable is uninitialized. For IEEE floating point this is free, since there are invalid bit patterns (NaN) that are checked by conforming hardware. To do this in general needs special hardware or expensive software checking.

Definite Assignment

Some languages, e.g., Java and C#, specify that the compiler must be able to show that no variable is used before given a value. If the compiler cannot confirm this, a warning/error message is produced.


Some languages, e.g., Java, C++, and C# permit the program to provide constructors that automatically initialize dynamically allocated variables.

6.1.4: Ordering within Expressions

Which addition is done first when you evaluate X+Y+Z? This is not trivial: one order might overflow or give a less precise result. For expressions such as f(x)+g(x) the evaluation order matters if the functions have side effects.

Applying Mathematical Identities

6.1.5: Short-Circuit Evaluation

Sometimes the value of the first operand determines the value of the entire binary operation. For example 0*x is 0 for any x; True or X is True for any X. Not evaluating the second operand in such cases is called a short-circuit evaluation. Note that this is not just a performance improvement; short-circuit evaluation changes the meaning of some programs: Consider 0*f(x), when f has side effects (e.g. modifying x or printing). We treat this issue in 6.4.1 (short-circuited conditions) when discussing control statements for selection.

Homework: CYU 7. What is an l-value? An r-value? CYU 11. What are the advantages of updating a variable with an assignment operator, rather than with a regular assignment in which the variable appears on both the left- and right-hand sides?

6.2: Structured and Unstructured Flow

Early languages, in particular, made heavy use of unstructured control flow, especially goto's. Much evidence was accumulated to show that the great reliance on goto's both increased the bug density and decreased the readability (these are not unrelated consequences) so today structured alternatives dominate.

A famous article (actually a letter to the editor) by Dijkstra entitled Go To Statement Considered Harmful put the case before the computing public.

Common today are

6.2.1: Structured Alternatives to goto

Common uses of goto have been captured by newer control statements. For example, Fortran had a DO loop (similar to C's for), but had no way other that GOTO to exit the loop early. C has a break statement for that purpose.

Homework: 24. Rubin [Rub87] used the following example (rewritten here in C) to argue in favor of a goto statement.

    int first_zero_row = -1     /* none */
    int i, j;
    for (i=0; i<n; i++) {
      for (j=0; j<n; j++) {
        if (A[i][j]) goto next;
      first_zero_row = i;
The intent of the code is to find the first all-zero row, if any, of an n×n matrix. Do you find the example convincing? Is there a good structured alternative in C? In any language?

Multilevel Returns

Errors and Other Exceptions

  begin                              begin
    x := y/z;                          x := y/z;
    z := F(y,z); -- function call      z := F(y,z);
    z := G(y,z); -- array ref          z := G(y,z);
  end;                               exception   -- handlers
                                       when Constraint_Error =>
                                         -- do something

The Ada code on the right illustrates exception handling. An Ada constraint error signifies using an incorrect value, e.g., dividing by zero, accessing an array out of bounds, assigning a negative value to a variable declared positive, etc.

Ada is statically scoped but constraints have a dynamic flavor. If G raises a constraint error and does not have its own handler, then the constraint error is propagated back to the caller of G, in our case the anonymous block on the right. Note that G is not lexically inside the block so the constraint error is propagating via the call chain not scoping.

    (define f
      (lambda (c)
    (define g
      (lambda ()
        ;; beginning of g
        (call-with-current-continuation f)
        ;; more of g

6.2.2: Continuations

A continuation encapsulates the current context, including the current location counter and bindings. Scheme contains a function

      (call-with-current-continuation f)
that invokes f, passing to it the current continuation c. The simplest case occurs if a function g executes the invocation above and then f immediately calls c. The result is the same as if f returned to g. This example is shown on the right.

However, the continuation is a object that can be further passed on and can be saved and reused. Hence one can get back to a given context multiple times. Moreover, if f calls h giving it c and h calls c, we are back in g without having passed through f.

One more subtlety: The binding that contained in c is writable. That is, the binding is from name to memory-location-storing-the name, not just from name to current-value. So in the example above, if f changes the value of a name in the binding, and then calls c, g will see the change.

Continuations are actually quite powerful and can be used to implement quite a number of constructs, but the power comes with a warning: It is easy to (mis-)use continuations to create extremely obscure programs.

6.3: Sequencing

    for I in 1..N loop
      -- loop body
    end loop;
Many languages have a means of grouping several statements together to form a compound statement. Pascal used begin-end pairs to form compound statements, C shortened it to {}. Ada doesn't need {} because the language syntax already contains bracketing constructs. As an example, the code on the right is the Ada version of a simple C for loop. Note that the end is part of the for statement and not a separate begin-end compound.

When declarations are combined with a sequence the result is a block. A block is written

    { declarations / statements }   declare declarations begin statements end
in C and Ada respectively. As usual C is more compact, Ada more readable.

Another alternative is make indentation significant. I often use this is pseudo-code; B2/ABC/Python and Haskell use it for real.

  if condition then statements
  if (condition) statement

  if condition then
  end if

  if condition_1 {
    statements_1 }
  else if condition_2 {
  else {

  if condition_1 then
  elsif condition_2 then
  elsif condition_n then
  end if

6.4: Selection

Essentially every language has an if statement to use for selection. The simplest case is shown in the first two lines on the right. Something is needed to separate the condition from the statement. In Pascal and Ada it is the then keyword; in C/C++/Java it is the parentheses surrounding the condition.

The next step up is if-then-else, introduced in Algol 60. We have already seen the infamous dangling else problem that can occur if statements_1 is an if. If the language uses end markers like end if, the problem disappears; Ada uses this approach, which is shown on the right. Another solution, used by Algol 60, is to forbid a then to be followed immediately by an if (you can follow then by a begin block starting with if).

When there are more than 2 possibilities, it is common to employ else if. The C/C++/Java syntax is shown on the right. This same idea could be used in languages featuring end if, but the result would be a bunch of end if statements at the end. To avoid requiring this somewhat ugly code, languages like Ada include an elsif which continues the existing if rather than starting a new one. Thus only one end if is required as we see in the bottom right example.

6.4.1: Short-Circuit Conditions (and Evaluation)

Can if (y==0 || 1/y < 100) ever divide by zero?
Not in C which requires short-circuit evaluation.

In addition to divide by zero cases, short-circuit evaluation is helpful when searching a null-terminated list in C. In that situation the idiom is

    while (ptr && ptr->val != value)
The short circuit evaluation of && guarantees that we will never execute null->val.

But sometimes we do not want to short-circuit the evaluation. For example, consider f(x)&&g(x) where g(x) has a desirable side effect that we want to employ even when f(x) is false. In C this cannot be done directly.

  if C1 and C2  -- always eval C2
  if C1 and then C2 -- not always

Ada has both possibilities. The operator or always evaluates both arguments; whereas, the operator or else does a short circuit evaluation.

Homework: 12. Neither Algol 60 nore Algol 68 employs short-circuit evaluation for Boolean expressions. In both languages, however, an if...then...else construct can be used as an expression. Show how to use if...then...else to achieve the effect of short-circuit evaluations.

  case nextChar is
    when 'I'    => Value := 1;
    when 'V'    => Value := 5;
    when 'X'    => Value := 10;
    when 'C'    => Value := 100;
    when 'D'    => Value := 500;
    when 'M'    => Value := 1000;
    when others => raise InvalidRomanNumeral;
  end case;

6.4.2 Case/Switch Statements

Most modern languages have a case or switch statement where the selection is based on the value of a single expression. The code on the right was written in Ada by a student of ancient Rome. The raise instruction causes an exception to be raised and thus control is transferred to the appropriate handler.

A case statement can always be simulated by a sequence of if statements, but the case statement is clearer.

(Alternative) Implementations

In addition to increased clarity over multiple if statements, the case statement can, in many situations be compiled into better code. Most languages having this construct require that the tested for values ('I', 'V', 'X', 'C', 'D', and 'M' above) must be computable at compile time. For example in Ada, they must be manifest constants, or ranges such as 5..8 composed of manifest constants, or a disjunction of these (e.g., 6 | 9..20 | 8).

In the simplest case where each choice is a constants, a jump table can be constructed and just two jumps are executed, independent of the number of cases. The size of the jump table is the range of choices so would be impractical if the case expression was an arbitrary integer. If the when arms formed consecutive integers, the implementation would jump to others if out of range (using comparison operators) and then use a jump table for the when arms.

Hashing techniques are also used (see 3e). In the general case the code produced is a bunch of tests and tables.

Syntax and Label Semantics

The languages differ in details. The words case and when in Ada become switch and case in C. Ada requires that all possible values of the case expression be accounted for (the others construct makes this easy). C and Fortran 90 simply do nothing when the expression does not match any arm even though C does support a default arm.

  switch (x+3) {
    case 2:  statements_2
    case 1:  statements_1
    case 6:  statements_6   // NO break between
    case 5:  statements_5   // cases 6 and 5
    default: something

The C switch Statement

The C switch statement is rather peculiar. There are no ranges allowed and the cases flow from one to the other unless there is a break, a common cause of errors for beginners. So if x is 3, x+3 is 6 and both statements_6 and statements_5 are executed.

Indeed, the cases are really just statement labels. This can lead to quite unstructured code including the infamous ...

Duff's Device (R Rated—Don't Show This to Minors)

  void send (int *to, int *from, int count) {
    int n = (count + 7) /8;
    switch (count % 8) {
      case 0: do { *to++ = *from++;
      case 7:      *to++ = *from++;
      case 6:      *to++ = *from++;
      case 5:      *to++ = *from++;
      case 4;      *to++ = *from++;
      case 3:      *to++ = *from++;
      case 2:      *to++ = *from++;
      case 1:      *to++ = *from++;
                 } while (--n > 0);

This function is actually perfectly legal C code. It was discovered by Tom Duff, a very strong graphics programmer, formerly at Bell Labs now at Pixar. The normal way to copy n integers is to loop n times. But that requires n tests and n jumps.

Duff unrolled the loop 8 times meaning that he looped n/8 times with each body copying 8 integers. Unrolling is a well known technique, nothing new yet. But, if n is say 804, then after doing 100 iterations with 8 copies, you need to do one extra iteration to copy the last 4. The standard technique is to write two loops, one does n/8 iterations with 8 copies, the other does n%8 iterations with one copy. Duff's device uses only one loop.

Compiler technology has improved since Duff created the device and Wikipedia says that when it was removed from part of the X-window system, performance increased.

6.5: Iteration

  for I in A..N loop statements end loop;
  for (int i=A; i<n; i++) { statements }

6.5.1: Enumeration-Controlled Loops

On the right we see the basic for loop from Ada and C.

Code Generation For for Loops

Possibilities / Design Issues (Semantic Complications)

There is a tension between flexibility and clarity. An Ada advocate would say that for loops are just for iterating over a domain; there are other (e.g., while) loops that should be used for other cases. A C advocate finds the for(;;) construct an expressive and concise way to implement important idioms.

This tension leads to different answers for several design questions.

  for (expr1; expr2; expr3) statements;

  while (expr2); {

6.5.2: Combination Loops

The C for loop is actually quite close to the while loops we discuss below since the three clause within the for can be arbitrary expressions, including assignments and other side-effects. Indeed the for loop on the right is defined by C to have the same semantics as the while loop below it unless the statements in the body include a continue statement.

Loops combining both enumeration and logical control are also found in Algol 60 and Common Lisp.

6.5.3: Iterators

We saw above an example of a loop with range an enum Weekday=(Mon,Tues,Wed,Thurs,Fri). More generally, some languages, e.g., Clu, Python, Ruby, C++, Euclid, and C# permit the programmer to provide an iterator with any container (an object of a discrete type).

    for (thing=first(); anyMore(); thing=next()); {

The programmer then writes first, next, and anyMore operators that in succession yield all elements of the container. So the generalization of the C for loop becomes something like the code on the right.

As an example imagine a binary tree considered as a container of nodes. One could write three different iterators: preorder, postorder, and inorder. Then, using the appropriate iterator in the loop shown above would perform a preorder/postorder/inorder traversal of the tree.

True Iterators

Iterator Objects

Iterating with First-Class Functions

Iterating without Iterators

Generators in Icon

6.5.5: Logically Controlled Loops

Essentially all languages have while loops of the general form

    while condition loop statements end loop;
In while loops the condition is tested each iteration before the statements are executed. In particular, if the condition is initially false, no iterations are executed.

In fact all loops can be expressed using this form, for suitable conditions and statements. This universality proves useful when performing invariant/assertion reasoning and proving programs consistent with their specifications.

  repeat statements until condition;
  do { statements } while (condition);

  first := true;
  while (first or else condition) loop
    first := false;
  end loop;

Post-test Loops

Sometimes we want to perform the test at the bottom of the loop (and thus execute the body at least once). The first two lines on the right show how this is done in Pascal and C respectively. The difference between the two is that the repeat-until construct terminates when the condition is true; whereas, the do-while terminates when the condition is false.

The do-while is more popular. One language that has neither is Ada so code like the bottom right is needed to simulate do-while.

  Outer: while C1 loop
    Mid: while C2 loop
      exit outer when condition_fini;
      inner:while C3 loop
        exit when condition_2b;
        exit mid when condition_1b;
      end loop inner;
    end loop mid;
  end loop outer;

Midtest Loops

More generally, we might want to break out of a loop somewhere in the middle, that is, at an arbitrary point in the body. Many languages offer this ability, in C/C++ it is the break statement.

Even more generally, we might want to break out of a nested loop. Indeed, if we are nested three deep, we might want to end the two innermost loops and continue with the outer loop at the point where the middle loop ends.

In C/C++ a goto is needed to break out of several layers; Java extends the C/C++ break with labels to support multi-level breaks; and Ada uses exit to break out and a labeled exit (as shown on the right) for multi-level breaks.

For example, if condition_fini evaluates true, the entire loop nest is exited; if condition_2b evaluates true, statements_2b is executed; and if condition_1b evaluates true, statements_1b is executed.

6.6: Recursion

We will discuss recursion again when we study subprograms. Here we just want to illustrate its relationship to iteration.

6.6.1: Iteration and Recursion

Although iteration is indicated by a keyword statement such as while, for, or loop, and recursion is indicated by a procedure/function invocation, the concepts are more closely related than the divergent syntax suggests. Both iteration and recursion are used to enable a section to be executed repeatedly.

  #include <stdio.h>
  int summation (int (*f)(int x), int low, int high) {
      int total = 0;
      int i;
      for (i = low; i <= high; i++) {
          total += (*f)(i);
      return total;
  int square(int x) {
      return x * x;
  int main (int argc, char *argv[]) {
      printf ("Ans is %d", summation(&square,1,3));
      return 0;

  (define square (lambda (x) (* x x)))
  (define summation
    (lambda (f lo hi)
       ((> lo hi) 0)
       ((= lo hi) (f lo))
     (else (+ (summation f lo (- hi 1)) (f hi))))))
  (summation square 1 3)

  (summation (lambda (x) (* x x)) 1 3)

On the right we see three versions of a function to compute

    Sum (from i = lo) (to i = hi) f(i)
given three parameters, lo, hi, and (a pointer to) f.
  1. The first version uses iteration. It is written in C, which does not support passing functions as parameters, but does support passing pointers to functions. This explains the specification of the first parameter of summation. As an aside, C is willing to do some automatic type conversions (called coercions) so the & could be omitted and the loop body could say f(i). However, it is important to note that the first argument to summation is a pointer not a function.

  2. The second version is in scheme and uses recursion. The reason the recursion goes down from hi rather than up from lo is so that the arithmetic is done in the same order as with the iterative version.

  3. The third version uses the same definition of summation as does the second, but uses the anonymous function directly and thus doesn't need (or have) any mention of the name square.

Tail Recursion

A recursive function is called tail recursive if the last thing it does before returning is to invoke itself recursively.

In this case we do not need to keep multiple copies of the local variables since, when one invocation calls the next, the first is finished with its copy of the variables and the second one can reuse them rather than pushing another set of local variables on the stack. This is very helpful for for performance.

  int gcc (int a, int b) {               int gcc (int a, int b) {
    if (a == b) return a;                  if (a == b) return a;
    if (a > b) return gcd(a-b,b);          if (a > b) {
                                             a = a-b; goto start; }
    return gcd(a,b-a); }                   b = b-a; goto start; }

The two examples on the right are from the book and show equivalent C programs for gcd: the left (tail-) recursive, the right iterative. Both versions assume the parameters are positive. Good compilers (especially, those for functional languages will automatically convert tail recursive functions into equivalent iterative form.

Thinking Recursively

I very much recommend the book The Little Schemer by Friedman and Felleisen.

6.6.2: Applicative and Normal-Order Evaluation

Lazy Evaluation

6.7: Nondeterminacy

Start Lecture #4

Chapter 7: Data Types

Will be done later.

Chapter 8: Subroutines and Control Abstraction

Subroutines and functions are perhaps the most basic and important abstraction mechanism. They are found in all general purpose languages and many special purpose ones as well.

Procedures vs. Functions: Applicative vs. Functional

Procedures are always applicative, they must be called for their side effects since they do not return a value.

In a purely functional model (e.g., Haskell) functions are called solely for their return value; they have no side effects. These functions are like their mathematical namesakes: they are simply a mapping from inputs to outputs.

Much more common is a hybrid model in which functions can have side effects.

8.1: Review of Stack Layout

I do not cover this in detail here, since we cover it in some depth in the compilers class. I just make a few points.

8.2: Calling Sequence

The caller and the callee cooperate to create a new AR when a subroutine is called. The exact protocol used is system dependent.

There are two parts to the calling sequence: the prologue is executed at the time of the call, the epilogue is executed at the time of the return.

Some authors use calling sequence just for the part done by the caller and use prologue and epilogue just for the part done by the callee. I don't do this.

Saving and Restoring Registers

Some systems use caller-save; others use callee-save; still others have some registers saved by the caller, other registers saved by the callee.

Maintaining the Static Chain

In addition to the dynamic link, another inter-AR pointer is kept: the static link (access link in dragon). The static link points to the AR of the most recent activation of the lexically enclosing block.

The static link is needed so that non-local objects can be referenced (local objects are in the current AR).

It is not hard to calculate the static link during (the prologue of) the calling sequence unless the language supports passing subroutines as arguments. We will not cover it; the simple case is again in the compiler notes.

A Typical Calling Sequence

As mentioned previously, the exact details are system dependent, what follows is a reasonable approximation.

The calling sequence begins with the caller:

  1. Pushes some registers on to the stack (modifying sp).
  2. Pushes arguments.
  3. Computes and pushes the static link.
  4. Executes a call machine instruction and pushes the return address.

Next the callee:

  1. Pushes the old frame ptr and calculates the new one.
  2. Pushes some registers.
  3. Allocates space for temporaries and local variables.
  4. Begins execution of the subroutine.

When the subroutine completes, the callee:

  1. Stores the return value (perhaps on the stack).
  2. Restores some registers.
  3. Restores the sp.
  4. Restores the fp.
  5. Jumps to the return address, resuming the caller.

Finally, the caller:

  1. Restores some registers.
  2. Moves the return value to its destination.

Homework: CYU 5

Special Case Optimizations

8.2.1: Displays

8.2.2: Case Studies: C on the MIPS; Pascal on the x86

8.2.3: Register Windows

8.2.4: In-Line Expansion

8.3: Parameter Passing

Much of the preceding part of this chapter concerned compilers and the implementation of programming languages. When we study parameters, in particular parameter modes, we are discussing the actual semantics of programs, i.e., what answers are produced rather than how is it implemented. However, many of the semantic decisions are heavily influenced by implementation issues.

Most languages use a prefix notation for subroutine invocation. That is, the subroutine name comes first, followed by the arguments. For applicative languages the arguments are normally parenthesized. As we have seen in Lisp (e.g., Scheme) the function name is simply the first member of list whose remaining elements are the arguments.

Another syntactic difference between most applicative languages and Lisp is that, in the former, built in language constructs look quite different from function application; whereas, in Lisp they look quite similar. Compare the following C and Scheme constructs for setting z to the min of x and y.

    if (x < y) z = x; else z = y;
    (if (< x y) (set! z x) (set! z y))
    (set! z (if (< x y) x y))

Formal parameters (often called simply parameters) are the names that appear in the declaration of the subroutine.
Actual parameters (often called simply arguments) refer to the expressions passed to a subroutine at a particular call site.

8.3.1 Parameter Modes

The mode of a parameter determines the relation between the actual and corresponding formal parameter. For example, do changes to the latter affect the former.

There are a number of modes including.

  int c = 1;
  printf ("%d\n");
  void f(int x) { x = 5; }


When using call-by-value semantics, the argument is evaluated (it might by an expression) and the value is assigned to the parameter. Changes to the parameter do not affect the argument, even when the argument is a variable. This is the mode used by C. Thus the C program on the right prints 1.

As most C programmers know, g(&x); can change the value of x in the caller. This does not contradict the above. The value of &x can not be changed by g(). Naturally, the value of &x can be used by g(). Since that value points to x, g() can use &x to change x.

As most C programmers know, if A is an array, h(A) can change the value of elements of A in the caller. This does not contradict the above. The extra knowledge needed is that in C writing an array without subscripts (as in h(A) above) is defined to mean the address of the first element. That is, h(A) is simply shorthand for h(&A[0]). Thus, the h(A) example is essentially the same as the g(&x) example preceding it.

In Java, primitive types (e.g., int) are passed by value. Although the parameter can be assigned to, the argument is not affected.


The location of the argument (the argument must be an l-value, but see the Fortran exception below) is bound the parameter. Thus changes to the parameter are reflected in the argument. Were C a call-by-reference language (it is not) then the example on the upper right would print 5.

By default pascal uses call-by-value, but if a parameter is declared var, then call-by-reference is used.

Fortran is a little weird in this regard. Parameters are normally call-by-reference. However, if the argument is not an l-value, a new variable is created, assigned the value of the argument, and is itself passed a call-by-reference.. Since this new variable is not visible to the programmer, changes by the callee to the corresponding parameter are not visible to the program.

In Java, objects are passed by reference. If the parameter is declared final it is readonly.

  with Ada.Integer_Text_IO; use Ada.Integer_Text_IO;
  procedure ScalarParam is
     A : Integer := 10;
     B : Integer;
     Procedure F (X : in out Integer; Ans : out Integer) is
        X := X + A;
        Ans := X * A;
     end F;
     Put (B);
  end ScalarParam;

  with Ada.Integer_Text_IO; use Ada.Integer_Text_IO;
  procedure Ttada is
     type IntArray is array (0..2) of Integer;
     A : IntArray := (10, 10, 10);
     B : Integer;
     Procedure F (X : in out IntArray; Ans : out Integer) is
        X(0) := X(0) + A(0);
        Ans  := X(0) * A(0);
     end F;
     Put (B);
  end Ttada;


With call-by-value/result the value in the argument is copied into the parameter during the call, and, at the return, the value in the parameter (the result) is copied back into the argument.

Certainly this copying is different from call-by-reference, but the effect seems to be the same: changes to the parameter during the subroutine's execution are reflected in the argument after the return.

However, in the face of aliasing, call-by-value/result can differ from call-by-reference as illustrated in the top Ada program on the right. Clearly the first assignment statement sets X to 20 which will, eventually, be transmitted back to A. The question is what value of A is used in the next assignment statement: call-by-reference says the value is 20 (so 400 is printed); whereas, call by value/result says the value is 10 (so 200 is printed).

In Ada, scalar arguments and parameters are call-by-value/result so the answer printed is 200. However, the program just below is basically the same, but uses arrays. In such cases the language permits either call-by-reference or call-by-value/result. The language reference manual warns users that programs, like this one, whose result depends on the choice between call-by-reference and call-by-value/result have undefined semantics. The ada implementation on my laptop (gnat) uses call-by-reference and therefore prints 400.

The Euclid language outlaws the creation of such aliases.


Call-by-name, made famous 40 years ago in Algol 60, will perhaps seem a little weird when compared to the currently more common modes above. In fact it is quite normal today, just not for subroutine invocation. One should compare it to macro expansion, for example #define in the C programming language. One should remember that in 1960, the most widely used programming languages were assembly languages and macro expansion was very common.

If the parameter is not encountered while executing the subroutine, the argument is not evaluated.

As in macro expansion, the argument is re-evaluated every time it is used. More significantly, it is evaluated in the context of the caller not the callee.

This last point causes significant difficulty for the implementation. Remember that the caller and callee are compiled separately. Thus the mechanism used is that the caller passes to the callee, not only the argument, which can be an expression, but also the referencing environment of the caller so that the expression can be evaluated correctly every time it is used. Traditionally, this referencing environment is called a thunk.

As C programmers know, it is wise to have extra parentheses; these were automatically supplied. Also the names of local variables in the callee were automatically made not to clash with the names of the parameters.


This can be view as a lazy approximation of call by name. The first time the parameter is encountered in the subroutine execution, the value is calculated and saved. This value is then reused if the parameter is encountered again.

Variations on Value and Reference Parameters

We discussed this above (call-by-value/result)


We do not discuss the subtle difference between call-by-sharing and call-by-reference.

The Purpose of Call-by-Reference

There are two reasons to use call-by-reference instead of call-by-value (assuming both are available).

  1. To enable the called routine to change the value of the argument.
  2. To save copying a large argument to the corresponding parameter. Be careful: it is often unwise to make semantic choices for performance reasons.

Read-Only Parameters

Some languages permit parameters to be declared readonly. This means that the value of the parameter cannot be altered in the callee, which naturally implies that the corresponding argument will not have its value changed.

Modula-3 actually uses the word READONLY; C and C++ use const.

Parameter Modes in Ada

An Ada programmer may declare each procedure parameter to have mode in, out, or in out. The default is in and all function parameters are required to have mode in. These modes do not describe the implementation (e.g., value vs. reference), which we have discussed above. Instead they describe the flow of information between caller and callee. That is, they concern semantic intent, not implementation.

As the names suggest, an in parameter can be read by the callee, but not written; whereas, an in out parameter can be both read and written. Both in and in out parameters initially have the value of the corresponding argument. In addition, the final value of an in out parameter, becomes the value of the argument when the procedure returns. Hence this argument must be an l-value.

An out parameter is similar to an in out parameter. The difference is that the initial value in the callee is undefined.

  int f (int x);
  int g (int *x);
  int h (int &x);

References in C++

Like C, C++ is a call-by-value language—but with a twist. Specific parameters can be declared to be call-by-reference simply by preceding their names with an &. Compare the three C++ function declarations on the right.

We have already seen examples like the first two before; they are both legal in C. The first one includes a standard call-by-value integer parameter x. Changes to x in f are not visible to the caller.

The function g contains a call-by-value pointer argument x (recall that int *x means that *x is an integer, so x is a pointer to an integer). The caller issues a statement such as g(&a), with a declared as an integer, theryby passing by value the pointer &a. Function g cannot change &a, but can use it to modify a. Again, this is completely standard call-by-value semantics.

The declaration of h is a C++ extension to C. Unlike the analogy to int *x (and many other C declarations), int &x does not mean that &x is an integer (that wouldn't make sense, what would x be?). Instead, it means that x is an integer that has been passed by reference. Similarly, the caller issues a statement such as h(a), with a declared an integer.

To a beginner, it is, at the least, surprising that both &a and *a, which in some ways have opposite semantics, here both are used to indicate that a can be changed by the caller.

  procedure main()
    procedure outer(i:int; procedure P)
      procedure inner
        print i
      end inner
      if i=1 then outer(2, inner)
      else P
    end outer
  begin main
  end main

Closures as Parameters (Passing Nested Subroutines)

Consider the code on the right, using lexical (static) scoping. Procedure outer has two parameters, the second of which f is itself a procedure. Procedure outer has a declaration of a nested procedure inner. The first time outer is invoked it calls itself passing the procedure inner. The second time outer is invoked, it calls its parameter P which is bound to inner.

When inner is called it must be able to reference not only its own declarations, but also the declarations in outer. Since outer is called twice, we must arrange for the correct outer environment to be visible to inner. Since, in this example the inner that is called was declared in the first invocation of outer, the value 1 is printed.

Note that the outer that actually called inner had i=2, but that is not relevant since we are assuming static scoping.

Thus the first outer must pass to the second one the referencing environment of inner (which, as we said, includes the declarations in outer). In other words, outer must pass to sub the closure of inner.

To repeat, nested procedures used as parameters complicates the picture considerably and necessitates the use of closures. A parameter that is a (pointer to a) non-nested procedure does not cause this problem. The nesting is required.

Functions that take other functions as arguments (and/or return functions as results are called higher-order functions. Programming languages that support functions taking function pointers as arguments (e.g. C) can emulate higher order functions.

Higher-order functions complicate the implementation, but we have not studied this. In particular, a parameter that is a (pointer to a) non-nested procedure does not cause the problem seen in the above example. Nesting is required for this problem to occur.

Homework: CYU 13, 14, 17.

Homework: 4, 6, 12.

8.3.2: Call-by-Name

Done previously.

8.3.3: Special-Purpose Parameters

Conformant Arrays

Default (optional) Parameters

Ada and C++ permit parameters to be specified with default values to be used if there is no corresponding argument.

    procedure f (x : integer; y : integer :=1) return integer;
    int f (int x, int y = 1);

Named Parameters

Ada permits the caller to refer to parameters by name (rather than position). Thus given the first declaration below, the two following invocations are equivalent.

    Function F (I : Integer; J : Integer; K : Integer);
    F(1, K=>3, J=>2);

Variable Numbers of Arguments

This is the famous varargs facility in C and friends.

    printf("X=%d, Y=%d, Z=%d, and W=%d\n", X,Y,Z,Z)
The number of subsequent arguments is determined by the first argument.

The caller knows how many arguments there are, but the callee code must be prepared for an arbitrary number. The solution is to have the frame pointer fp point to a location a fixed distance from the first argument. Then the prologue code in the callee can find this argument, determine the number of additional arguments, and proceed from there.

8.3.4: Function Returns

8.3.A: Parameter Passing in Certain Languages

There is no new material here. Instead, some previous material is reorganized by language rather than by concept.

Parameter Passing in C

Parameters are passed by value; the argument cannot be changed by the callee. Call by reference can be simulated by using pointers.

Readonly parameters can be declared using const

Parameter Passing in C++

Default is call-by-value as in C. Call by reference can be simulated with pointers as in C but can also be explicitly stated using &

Readonly parameters can be declared using const

Parameter Passing in Java

Primitive types (e.g., int) are passed by value; objects by reference. A parameter of primitive type can be assigned to but the argument is not affected.

A object parameter can be declared readonly via final.

Parameter Passing in Ada

Semantic intent is separated from implementation. The specific modes available to the programmerin, out, and in out determine the former, not the latter.

Functions (as opposed to procedures) can have only in parameters.

8.4: Generic Subroutines and Modules

8.5: Exception Handling

8.6: Coroutines

8.7: Events

8.A: First-Class Functions

Another complication is first-class functions, that is having the ability to construct functions dynamically and treat them similarly to how other data types are treated.

One difficulty is that activation records are no longer stack like. If procedure P builds function F dynamically and then returns it, the activation record for P must be maintained since F may be called later and needs the referencing environment of P where it was created (assuming lexical scoping).

We shall see first-class functions in Scheme.

8.B: Recursion

As we have stated previously, it is the possibility of recursion that forbids static allocation of activation records and forces a stack-based mechanism.

Start Lecture #5

Remark: The room for the final exam has been moved (by the department) from here 102 down the hall.

Chapter 9: Data Abstraction and Object Orientation

Done later.

Chapter 10: Functional Languages

10.1: Historical Origins

The pioneers in this work were mathematicians who were interested in understanding what could be computed. They introduced models of computation including the Turing Machine (Alan Turing) and the lambda calculus (Alonzo Church). We will not consider the Turing machine, but will speak some about the lambda calculus from which functional languages draw much inspiration.

10.2: Functional Progamming Concepts

Definition: The term Functional Programming refers to a programming style in which every procedure is a (mathematical) function. That is, the procedure produces an output based on its inputs and has no side effects.

The lack of side effects implies that no procedure can, for example, read, print, modify its parameters, or modify external variables.

By defining the stream from which one reads to be another input, and the stream to which one writes to be another output, we can allow I/O.

Some languages, such as Haskell are purely functional. Scheme is not: it includes non functional constructs (such as updating the value of a variable), but supports a functional style where these constructs are used much less frequently than in a typical applicative language.

Interesting Features found in Functional Languages

Having just described what is left out (or discouraged) in functional languages, I note that such languages, including Scheme, have powerful features not normally found in common imperative languages.

Homework: CYU 1, 4, 5.

Remark: We are moving the λ-calculus before Scheme unlike the 3e.

10.6: Theoretical Foundatons

10.6.1: Lambda Calculus

Our treatment is rather different from the book.

The λ-calculus was invented by Alonzo Church in 1932. It forms the underpinning of several functional languages such as Scheme, other forms of Lisp, ML, and Haskell.

Technical point: There are typed and untyped variants of the lambda calculus. We will be studying the pure, untyped version. In this version everything is a function as we shall see.


The syntax is very simple. Parentheses are NOT use to show a function applied to its arguments. Let f be a function of one argument and let x be the argument we want to apply f to. This is written fx, no blank, no comma, no parens, nothing.

Parentheses are used for grouping, as we show below. Ignoring parens for a minute the grammar can be written.

    M → λx.M                a function definition
      | MN                  a function application
      | identifier          a variable, normally one character

The functions take only one variable, which appears before the dot; the function body appears after the dot. For example λx.x is the function that, given x, has value x. This function is often called the identity.

Another example would be λx.xx. This function takes the argument x and has value xx. But what is xx? It is an example of the second production in the above grammar. The form xx means apply (the function) x to (the argument) x. Since essentially everything is a function, it is clear that functions are first-class values and that functions are higher order.

Below are some examples shown both with and without parentheses used for grouping.

    xxx                        (xx)x
    λx.xx                      λx.(xx)
Examples on the same line are equivalent (i.e., the parens on the right version are not needed). The default without parens is that xyz means (xy)z, i.e., the function x is applied to the argument y producing another function that is applied to the argument z. The variables need not be distinct, so xxx is possible. The other default is that the body of a function definition extends as far as possible.

Free and Bound Variables



Since nearly everything is a function, it is expected that function application (applying a function to its argument) will be an important operation.

Definition: In the λ-calculus, function application is called β-reduction. If you have the function definition λx.M and you apply it to N, (λx.M)N, the result is naturally M with the x's changed to N. More precisely, the result of β-reduction applied to (λx.M)N is M with all free occurrences of x replaced by N.

Technical point: Before applying the β-reduction above, we must ensure (using α-conversions if needed) that N has no free variables that are bound in M.

Do this example on the board: The β-reduction of λx.(λy.yx)z is (λy.yz)=λy.yz

To understand the technical point consider the following example λx.(λz.zx)z. First note that this is really the same example as all I did to the original is apply an α-transformation (y to z). But if I blindly apply the rule for β-reduction to this new example, I get (λz.zz)=λz.zz, which is clearly not equivalent to the original answer. The error is that in the new example M=(λz.zx), N=z, and hence N does have a free variable that is bound in M.

Order of Evaluation

Consider the C-language expression f(g(3)). Of course we must invoke g before we can invoke f. The reason is that C is call-by-value and we can't call f until we know the value of the argument. But in a call-by-name language like Algol-60, we call f first and call g every time (perhaps no times) f evaluates its parameter

Let's write this in the λ-calculus. Instead of 3, we will use the argument λx.yx (remember arguments can be functions) and for f and g we use the identity function λx.x. This gives (λx.x)((λx.x)(λx.yx)).

At this point we can apply one of two β-reductions, corresponding to evaluating f or g.

Definition: The normal order evaluation rule is to perform the (leftmost) outermost β-reduction possible.

Definition: The applicative order evaluation rule is to perform the (leftmost) innermost β-reduction possible.

Does the Order of Evaluation Matter

Doing one reduction using normal-order evaluation on our example gives an answer of ((λx.x)(λx.xy)). The outer (redundant) parentheses are removed and we get (λx.x)(λx.xy).

If, instead, we do one applicative-order reduction we get the same answer, but it seems for a completely different reason. Must the answers always be the same?

Church Rosser

Do it again in class with the following more complicated example. (λx.λy.yxx)((λx.x)(λy.z)).

In this case doing one normal-order reduction gives a different answer from doing one applicative-order reduction. But we have the following celebrated.

Church-Rosser Theorem: If a term M can be β-reduced (in 0 or more steps) to two terms N and P, then there is a term Q so that both N and P can be β-reduced to Q.

Corollary: If you start with a term M and keep β-reducing it until it can no longer be reduced, you will always get the same final term.

Definition: A term than cannot be β-reduced is said to be in normal form.

Continue on the board to find the normal form of (λx.λy.yxx)((λx.x)(λy.z)).

Does Every Term Have a Normal Form

That is, if you keep β-reducing, with the process terminate?

No. Consider (λx.xx)(λx.xx).


Were this a theory course we would rigorously explain the notion of computability. In this class, we will be content to say that roughly speaking computability theory studies the following question: Given a model of computation, what functions can be computed? There are many models of computation. For example we could ask for all functions computable

A fundamental result is that for all the models listed the same functions are computable.

Definition: Any model for which the computable functions are the same as those for a Turing Machine is called Turing Complete.

Thus, the fundamental result is that all the models listed above are Turing Complete. This should be surprising! How can the silly λ-calculus compute all the functions computable in Ada; after all the λ-calculus doesn't even have numbers?? Or Boolean values? Or loops? Or Recursion?

I will just show a little about numbers.

Numbers in the λ-Calculus

First remember that the number three is a concept or an abstraction. Writing three as three, or 3, or III, or 0011 does not change the meaning of three. What I will show is a representation of every non-negative number and a function that corresponds to adding 1 (finding the successor). Much more would be shown in a theory course.

It should not be surprising that each number will be represented as a function taking one argument—that is all we have to work with! The function with one parameter representing the number n takes as argument a function f and returns function g taking one argument. The function g will apply f n-times to its argument.

I think those words are correct, but I also think the following symbolism is clearer.

0 is represented as λf.λx.x 1 is represented as λf.λx.fx 2 is represented as λf.λx.f(fx) 3 is represented as λf.λx.f(f(fx)

So how do we represent the successor function S(n)=n+1? It must take one argument n and produce a function that takes an argument f and yields a function that applies f one more time to its argument than n does.

Again the symbols may be clearer than the words
S is λn.λf.λx.f(nfx)

Show on the board that S1 is 2, i.e., show that
(λn.λf.λx.f(nfx))(λf.λx.fx) is λf.λx.f(fx)

To make it clearer, first perform α-conversion to λf.λx.fx and get λg.λy.gy

Functions of More Than One Argument (Currying)

First note that in the expression λx.λy.z, the left most function is higher order. That is, it is given an argument x and it produces a function λy.z.

Given (higher-order) functions of one variable, it is easy to define functions of multiple variables by
λxy.z = λx.λy.z
This adds no power to the λ-calculus, but does make for shorter notation. For example, the successor function above is now written
S is λnfx.f(nfx).

10.6.2: Control Flow

This section shows how to model Boolean values, if-then-else, and recursion. Although I find it very pretty, I am skipping it.

10.6.3: Structures

This section shows how to model the Scheme constructs for list processing, from which one can build many other structures. Again, I am skipping it.

10.3: A review/Overview of Scheme

Lisp is one of the first high level languages, invented by John McCarthy in 1958 while at MIT (McCarthy is mainly associated with Stanford). Many dialects have appeared. Currently, two main dialects are prominent for standalone use: Common Lisp and Scheme. The Emacs editor is largely written in a Lisp dialect (elisp) and elisp is used as a scripting language to extend/customize the editor.

Whereas, Common Lisp is a large language, Scheme takes a more minimalist approach and is design to have clear and simple semantics.

Scheme was first described in 1975, so had the benefit of nearly 20 years of Lisp experience.

Notable Properties of Scheme

Interacting with Scheme

Scheme interpreters execute a read-eval-print loop. That is, the interpreter reads an expression, evaluates the expression, and prints the result, after which it waits to read another expression.

It is common to use ⇒ to indicate the output produced. Thus instead of writing
If the user types (+ 7 6), the interpreter replies 13
authors write (+ 7 6) ⇒ 13. I follow this convention. Note that the interpreter itself does not print ⇒; it simply prints the answer 13 in the previous example.

Try ssh access.cims.nyu.edu; then ssh mauler; then mzscheme. Illustrate the above in mzscheme. There is a drscheme environment, you may wish to investigate.

Remark: Remember that you may implement labs on any platform, but they must run at on the class platform, which is mzscheme.

Scheme Syntax

The syntax is trivial; much simpler than other languages you know. Every object in scheme is either an atom, a (dotted) pair, or a list. We will have little to say about pairs. Indeed, some of the words to follow should be modified to take pairs into consideration.

An atom is either a symbol (similar to an identifier in other languages) or a literal. Literals include numbers (we use mainly integers), Booleans (#t and #f), characters (#\a, #\b, etc.; we won't use these much), and strings ("hello", "12", "/usr/lib/scheme", etc.).

Symbols can contain some punctuation characters; we will manly use easy symbols starting with a letter and containing letters, digits, and dash (the minus sign). For example, x23 and hello-world are symbols.

A list can be null (the empty list); or a list of elements, each of which can be an atom or a list. Some example lists:

    ()                       (a b c)
    (1 2 (3))                ( () )
    (xy 2 (x y ((xy)) 4))    ( () "")
    ( () () )                ( (()) (""))

Note that nested lists can be viewed as trees (the null list is tricky).

Evaluating Expressions in Scheme

Evaluating Atoms in Scheme

Literals are self-evaluating, i.e., they evaluate to themselves.

    453 ⇒ 453
    "hello, world" ⇒ "hello, world"
    #t ⇒ #t
    #\8 ⇒ #\8

Symbols evaluate to their current binding, i.e., they are de-referenced as in languages like C. This concludes atoms.

Evaluating Lists in Scheme

A list is a computation. There are two types.

What if you want the list itself (or a symbol itself), e.g., what if you want the data item ("hello" hello) and don't want to evaluate "hello" on hello (indeed "hello" is not an operation so that would be erroneous)? Then you need a special form, in this case the form quote.

Some Standard (non-Special) Functions

We have already seen a few scheme functions I remember define, lambda, + from lecture one. The third one + is not a special form. The symbol + evaluates to a function and the remaining elements of its list evaluate to the arguments. The function + is invoked with these arguments and the result is their sum.

Some Scheme Type-Predicate Functions

Since values are typed, but symbols are not, programs need a way to determine the type of the value current bound to the symbol.

The book The Little Schemer recommends

    (define atom? (lambda (x) (and (not (pair? x)) (not (null? x)))))
I do this personally and sometime forget that atom? is not part of standard Scheme.

Two other predicates are very useful as well

Some Scheme (Special) Forms


    (+ x x)       (lambda (x) (+ x x))
The second does not evaluate any of the x's. Instead it does something special; in this case it creates an unnamed function with one parameter x and establishes the body to be (+ x x). No addition is performed when the lambda is executed (it is performed later when the created function is invoked).

Quoting Data

Problem: Every list is a computation (or a special form). How do we enter a list that is to be treated as data?
Answer: Clearly we need some kind of quoting mechanism.

Note that "(this is a data list)" produces a string, which is not a list at all. Hence the special form (quote data).

Quoting is used to obtain a literal symbol (instead of a variable reference), a literal list (instead of a function call), or a literal vector (we won't use vectors). An apostrophe ' is a shorthand.

    (quote hello)                 ⇒ hello
    'hello                        ⇒ hello
    (quote (this is a data list)) ⇒ (this is a data list)
    '(this is a data list)        ⇒ (this is a data list)

10.3.1: Bindings

Scheme has four special forms for binding symbols to values: define is used to give global definitions and the trio let, let*, letrec are used for generating a nested scope and defining bindings for that scope.

  (define x y)
  (define x "a string")
  (define f (lambda (x) (+ x x)))

On the right we see three uses of define. The special part about define is that it does NOT evaluate its first parameter. Instead, it binds that parameter to the value of the 2nd parameter, which it does evaluate. The form define cannot be used in a nested scope; One can redefine an existing symbol so the first two functions on the right can appear together as listed. The third function is not any different from the first two: the symbol f is bound in the global scope to the value computed by the 2nd argument, which just happens to be a function.

All three let variations have the same general form

    (let                     ; or let* or letrec
      ( (var1 init1) (var2 init2) ... (var initn) )
      body    )              ; this ) matches (let
For all three variations, a nested environment is created, the inits are evaluated, and the vars are bound in to the values of the inits. The difference is in the details, in particular, in the question of which environment is used when.

For let, all the inits are evaluated in the current environments, the new (nested) environment is formed by adding bindings of the vars to the values of the inits. Hence none of the inits can use any of the vars. More precisely, if an init mentions a var it refers to the binding than symbol had in the current (pre-let) environment.

For let*, the inits are evaluated and the corresponding vars are bound in left to right order. Each evaluation is performed in an environment in which the preceding vars have been bound. For example, init3 can use var1 and var2 to refer to the values init1 and init2.

For letrec a three step procedure is used

  1. All the vars are bound to uninitialized values.
  2. In this new environment, all the inits are evaluated (in an unspecified order).
  3. Each var is then assigned the value of the corresponding init.

  (letrec ((fact
            (lambda (n)
              (if (zero? n) 1
                 (* n (fact (- n 1)))))))
          (fact 5))

Thus any init that refers to any var is referencing that var's binding in the new (nested) scope. This is what is needed to define a recursive procedure (hence the name letrec). The factorial example on the right prints 120 and then exits the nested scope so that typing (fact 5) immediately after produces an error.

10.3.2: Lists and Numbers

There are three basic functions and one critical constant associated with lists.

  1. (car l) ; returns the first element of the list l.
  2. (cdr l) ; returns the rest of l.
  3. (cons x l) ; prepends x onto l.
  4. () ; the null list (a constant).
In addition, some Schemers execute (define nil '()) so that nil instead of '() can be used when the null list is desired.

  (car '(this "is" a list 3 of atoms)) ⇒ this
  (cdr '(this (has) (sublists))) ⇒ ((has) (sublists))
  (car '(x)) ⇒ x   (cdr '(x)) ⇒ '()
  (car '()) ⇒ error   (cdr '()) ⇒ error

List Decomposition: Car and Cdr

Another pair of names for car and cdr is, head and rest. The car is the head of the list (the first element) and the cdr is rest. If we continue (for just a little while longer) to ignore pairs, then we can say thatcar and cdr are defined only for non-empty lists and that cdr returns a (possibly empty) list.

List Decomposition Shortcuts: Cadr and Friends

Note that (car (cdr l)) gives the second element of a list and hence is a commonly used idiom. It can be abbreviated (cadr l). In fact any combination of cxxxxr with each x (no more than 4 allowed) an a or d is defined.

For example, (cdadar l) is (cdr (car (cdr (car l))))

  (cons 5 '(5)) ⇒ (5 5)   (cons 5 '()) ⇒ (5)
  (cons '() '()) ⇒ (())   (cons 'x '(()) ⇒ (x ())
  (cons "these" '("are" "strings")) ⇒ ("these" "are" "strings")
  (cons 'a1 (cons 'a2 (cons 'a3 '()))) ⇒ (a1 a2 a3)

List Building: Cons

The function (cons x l) prepends x onto the list l. It may seem that to get a list with 5 elements we need 5 cons applications, but there is a shortcut.

The function list takes any number of arguments (including 0) and returns a list of n elements. It is equivalent to n cons applications the rightmost having '() as the 2nd argument. For example
(list 'a1 'a2 'a3) is equivalent to (cons 'a1 (cons 'a2 (cons 'a3 '()))), the last example on the right.

(Dotted) Pairs, Improper Lists, and Box Diagrams

You could very easily ask where the silly names car and cdr came from. Head or first make more sense for car, and rest makes more sense for cdr. In this section I briefly cover the historical reason for the car/cdr terminology, hint at how lists are implemented, introduce (dotted) pairs, show how our (proper) lists as well as improper lists can be built from these pairs and present box diagrams, which are another (this time pictorial) representation of lists, pairs, and improper lists.

Lisp was first implemented on the IBM 704 computer that had 32,768 36-bit words. Since 32,768 is 215, 15-bits were needed to address the words. A common instruction format (all instructions were 36-bits) had 15-bit address and decrement fields. There also were address and decrement registers. Typically, the pointer to the head of a list was the Contents of the Address Register (CAR) and the pointer to the rest was the Contents of the Decrement Register. The car and cdr were stored in the address and decrement fields of memory words as well.

Thus we see that the fundamental unit is a pair of pointers in lisp. This is precisely what cons always returns.

Box diagrams are useful for seeing pictorially what a list looks like in terms of cons cells. The referenced page is from the manual for emacs lisp, which has some minor differences from scheme. I believe the diagram is completely clear if you remember than nil is often used for the empty list ('() in Scheme).

Each box-pair or domino depicts a cons cell and is written in Scheme as a dotted pair (each box is one component of the pair).

Note how every list (including sublists) ends with a reference to nil in the right hand component of the rightmost domino. This corresponds to the fact that if you keep taking cdr (cddr, cdddr, etc) of any list you will get to '() and then cdr is invalid. This is the defining characteristic of a proper list (normally called simply a list).

In fact the second argument to cons need not be a list. The single domino improper list beginning this writeup on dotted pairs shows the result of executing (cons 'rose 'violet). In this example the second argument is an atom, not a list. The resulting cons cell can again be written as a dotted pair, in this case it is (rose . violet). Likewise, cdr is generalized to take this domino as input. As before it returns the right hand box of the domino, in this case violet. Thus we maintain the fundamental identities
(car (cons a b)) is a   and   (cdr (cons a b)) is b
for any objects a and b. Previously b had to be a list.

The summary is that cons in generalized to not require a list for the second argument, the resulting object is represented as a dotted pair in scheme, and linking together dotted pairs gives a generalized list. If the last dotted pair in every chain has cdr equal to '(), then the generalized list is an ordinary list; otherwise it is improper.

(define (make-counter)
  (let ((count 0))
    (lambda () (set! count (+ 1 count))
(define ctr1 (make-counter))
(define ctr2 (make-counter))

Closures (not covered 2009-10 Fall)

Consider the code on the right. When the first (define is executed, a new function make-counter is created. This is nothing new. However, bundled with the function is the variable count. Technically, count is part of the environment in which the function is defined and the (define captures the entire closure (i.e., the environment is included).

When the second and third (define's are executed, two counters are created, each of which is initialized to zero and will grow each time they are invoked. thus the 5 function invocations that follow will print 1,2,3,4,2.

Homework: CYU: 9.

Homework: 1, 3.

Start Lecture #6

Remark: Review the meaning of free in M.

Remark: Lab 2 was assigned last thursday it is due 22 October 2009.

Remark: The midterm will be during recitation #8, 26 Oct. A practice final is available.

Numbers in Scheme

Scheme has a wide variety of numbers, including rational number (i.e., fractions). Most implementations include arbitrary precision rationals. We will stick to simple integers.

Vectors in Scheme

A vector is similar to an array but the elements may be of heterogeneous type, similar to a record in Ada or a struct in C. We will not study vectors.

10.3.3: Equality Testing and Searching

Boolean Values

Scheme has two Boolean constants, #t and #f. However, when performing a test (e.g., in a control flow structure) any value not #f is treated as true.

Eq?, Eqv? and Equal?

Scheme has three general comparison operators, the first two of which are similar. We will use only eq? and equal?.

  (eq 'a 'a)
  (let ((x 3) (y 3)) (eq? x y))
  (let ((x '()) (y '())) (eq? x y))
  (eq? 'x 'y) ⇒ #f
  (eq? (cons 2 3) (cons 2 3)) ⇒ #f
  (eq? "" "")  (eq "xy" "xy")
  (eq? 2 2)
  (eq? '(2 . 3) '(2 . 3))  ⇒ #t
  (eq? (list 2 3) (list 2 3)) ⇒ #f
  (equal? (lambda (x) x) (lambda (x) x))

The first eq? is cheap, it essentially just checks the memory address.
So (eq 'a 'a) ⇒ #t because Scheme only keeps one symbol a. (If two locations had the symbol a, how could a be evaluated?). Similarly, Scheme keeps only one constant 3 so the second example on the right yields #t. There is also only one empty list so the next example also gives #t. Naturally the two symbols x and y are stored separately, explaining the next example. Each cons invocation produces a new cons cell (a new dotted pair), explaining the next examples.

Implementations are given freedom on how to store strings so there might be two copies of the same string. Hence the next two examples (on one line) have undefined results. They will, however, be either #t or #f. The same is true for numbers (next example, eqv? would give #t). Even though the dotted pair example that is next looks just like the cons example, this time the result is unspecified. The principle here is that the implementation is free to store all uses of the same literals in the same location. The next example is not a literal, but a cons (actually 2 cons) so the result is #f.

The story for equal? is simpler. A good rule of thumb is that if the two arguments print the same, equal? evaluates to #t; otherwise #f. The only unspecified case is for functions is the last example (both mxscheme and scheme48 give #f)

We will show searching below, after introducing cond.

     (pred1 expr1)
     (pred2 expr2)
     (predn exprn)
     (else def-expr)

10.3.4: Control Flow and Assignment

Control Flow: Cond and If

Scheme actually has a number of special forms for control flow. However, you can get by with just one, cond, a case/switch-like construct, which is shown on the right. It is a special form since not all arguments to cond are necessarily evaluated and those that are evaluated have a special rule.

First pred1 is evaluated, if the value is not #f, it is considered true and expr1 is evaluated. This completes the evaluation of the cond; the value of the cond is the value of expr1.

If pred1 evaluates to #f, the same procedure is applied to pred2, etc.

If all n predi's evaluate to #f, def-expr is evaluated and that becomes the value of the cond.

For simple tests if is convenient

    (if (condition) (exprt) (exprf))
Again we have a special form. First condition is evaluated. If the value is #t, exprt is evaluated and that becomes the value of the if. If condition evaluates to #f, then exprf is evaluated and that becomes the value of the if.

Control Flow: Sequencing and Do Loops


What do you do when the then part of your code if is more than one expression? Or if this happens to one of the expri's in a cont? You need a grouping similar to {} in C. You could use one of the let's, but that is overkill when you don't need a new scope. Instead you use the begin special form shown on the right. The expri's in the begin form are all executed in order and the value of the last one is the value of the begin.

The basic mechanism for iteration is recursion, but various looping constructs are also available

I guess the keyword do was chosen for the looping form because lisp was invented around the time of Fortran, but I do not know. You can always use recursion instead of a do loop; but the do loop is in the book if you want to use it.

Assignment: The Bangs

The special forms involving assignment end in !, which is pronounced bang (at least set! is pronounced set-bang; I am not so sure about set-car! and set-cdr!.

The (side-) effect of set! is to change the value of its first argument to the value of its second argument. Again we have a special-form since the first argument is not evaluated. It is an error to set bang an undefined identifier; for that you should use define or one of the let's.

The special functions set-car! and set-cdr! change the car (resp. cdr) fields of their first argument to the value of the second argument. I advise against their use, as the results can be surprising. For some reason, they don't appear to be available in mzscheme. There are in scheme48 and also definitely appear in the scheme manual so I am surprised. But their absence is no great loss for us. Ang found the missing mzscheme bangs. They are part of a group of mutable functions including mcons that makes a cons cell you can mutate with the mutable bangs.

Recursion on Lists

Lists are the basic Scheme data structure and recursion is the basic iteration construct so it is important to see how to use recursion to step through the elements of a loop.

  (define member
    (lambda (elem lat)
        ((null? lat) #f)
        ((eq? elem (car lat)) lat)
        (else (member elem (cdr lat))))))

(define count-members
  (lambda (a lat) (member1 a lat 0)))
(define member1
  (lambda (a lat count)
     ((null? lat) count)
     ((eq? a (car lat))
      (member1 a (cdr lat) (+ 1 count)))
     (else (member1 a (cdr lat) count)))))

As an example the code on the right implements member, a function with two parameters: elem an element and lat a list of atoms (i.e., lat is a list and each element is an atom, no sublists). If the element does not appear in the list, member returns #f. It it does appear, member returns the suffix of the list starting with the first occurrence. We could return #t, but the above is more common. Recall that everything except #f is viewed as true when testing, so returning either the suffix or #t has the same effect when just testing. Sometimes it is helpful to have the rest of the list in hand, perhaps for further searching.

The code sequence shown is fundamental for list operations, be sure you understand it completely. The second example counts the number of times a occurs in lat

This version of the program uses eq? for the testing. We might want instead to use equal? or even eqv?. Thus we could write three versions of member and count-members just changing eq? to equal? and then to eqv?. A better alternative is to use higher-order functions, as shown below.

(define count-members-sexp
  (lambda (a s) (member2 a s 0)))
(define member2
  (lambda (a s count)
     ((null? s) count)
     ((atom? (car s))
       ((eq? a (car s))
        (member2 a (cdr s) (+ 1 count)))
       (else (member2 a (cdr s) count))))
     (else ;; the car is a sublist
      (+ (member2 a (car s) 0)
         (member2 a (cdr s) count))))))

Nested Lists and S-Expressions

An element of a list can itself be a list, for example (1 (2 3)). More generally a parenthesized sequence of symbols, with balanced parenthesis is called an s-expression. How do we write a program that can deal with a list containing sublists? The code on the right does this, again counting occurrences. It assumes the sexp is a list possibly with sub-lists. But it doesn't handle the case where the sexp is just an atom.

Homework: First, enhance the last example to handle atoms as well. Second, change the example code and your enhancement to use if instead of cond where a simple if-then-else is appropriate.

10.3.5: Programs as Lists

As mentioned previously, Scheme (and Lips in general) is homiconic, or self-representing. A parenthesized sequence of symbols, with balanced parenthesis is called an s-expression whether the sequence represents a list or a program. Indeed, an unevaluated program is exactly a list.

We have seen that Scheme has a special form quote that prevents evaluation. In addition there is a function eval that forces evaluation.

  (define fact-iter
    (lambda (prod lo hi)
      (if (> lo hi)
          (fact-iter (* prod lo)
                     (+ lo 1)
  (define fact-tail (lambda (n) (fact-iter 1 1 n)))

10.3.A: Tail-Recursion Revisited

We have already noted that if the last action performed by a function is a recursive call of itself (and there are no other direct or indirect recursive calls of this function), then a new AR is not needed and the recursion can be transformed into iteration by a compiler.

The only new point to be made here is that sometimes a clever programmer can turn a seemingly fully recursive program into a tail-recursive one, often by defining an auxiliary (a.k.a. helper) function. We begin with the fact procedure fact shown above when discussing letrec above. That fact executes a multiply after evaluating its recursive call and thus is nottail recursive; however the transformed version on the right is.

Homework: CYU 10.

Homework: 6, 8.

10.3.6: Extended Example: DFA Simulation

10.4: Evaluation Order Revisited

10.4.1: Strictness and Lazy Evaluation

10.4.2: I/O Streams and Monads

10.5: Higher-Order Functions (i.e., Functions as Arguments and Return Values)

Assume you need three functions for a physics research project. All of them take as input the state of a system. The first function returns the heaviest object, The second function returns the densest object, The third function returns the object having the highest kinetic energy. You could write three separate programs, but you notice that they are all the same: they are determining a max but have different definitions of less than. Hmmm.

  (define make-member
    (lambda (test?)
      (lambda (elem lis)
          ((null? lis) #f)
          ((test? elem (car lis)) lis)
          (else (member elem (cdr lis)))))))

  (define member-eq    (make-member eq?))
  (define member-eqv   (make-member eqv?))
  (define member-equal (make-member equal?))

Returning to the member example above, we want variants with different comparison functions. So let's pass in the desired comparison function and use that. In more detail, write make-member as a function with one input, the comparison function. Make-member returns as result a function equivalent to member above but using the given comparison function instead of having eq? hard-wired in.

The result is shown on the right. Again, this is a fundamental use of first-class functions, be sure you understand it.

10.7: Functional Programming in Perspective

There is some evidence that functional programs are less buggy.

There is even greater evidence that applicative programming dominates in the real world. The question is why. Current believe is that the reason is social not technical: more courses, textbooks, libraries, etc.

Chapter 7: Data Types

We can think of a type as a set of values (the members of the type). A different question is how should the type be represented on the computer (Intel Floating Point, IBM Floating Point, vs. IEEE Floating Point; two's complement vs. one's complement for negative integers; binary vs. hexadecimal vs. octal). We will not discuss this second question.

Types can give implicit meaning to operations. For example in Java + means concatenation if the operands are strings and means addition if the operands are integers.

Type clashes, using a type where it is not permitted, often indicates a bug and, if checked automatically by the run-time system or (better, yet) by the compiler, can be a big aid in debugging.

7.1: Type Systems

A type system consists of:

The synthesis/inference terminology is not standardized. Some texts, e.g., 3e, use type inference both for determining the type of the whole from the type of its parts, and for determining the type of the parts from the type of the whole. Other texts, e.g., the Dragon book, use type synthesis for the former and type inference for the latter.

Some languages are untyped (e.g., B the predecessor of C); we will have little to say about those beyond saying that B actually had one datatype, the computer word.

Types must be assigned to those constructs that can have values or that can refer to objects that have values. These include.

7.1.1: Type Checking

Definition: Type checking is the process of ensuring that a program obeys the type system's type compatibility rules.

Definition: A violation of the type checking rules is called type clash.

Definition: A language is called strongly typed if it prevents operations on inappropriate types.

Definition: A strongly typed language is called statically typed if the necessary checks can be performed at compile time.

Note that static typing is a property of the language not the compiler: A statically typed language could have a poor compiler that does not perform all the necessary checks.

int main (void) {
    int *x;
    int y;
    int *z;
    z = x + y;
    return 0;

(define test
  (lambda ()
    (let ((x 5) (y '(4)))
      (+ x y))))

procedure Ttada is
   X : Integer := 1;
   type T is access Integer;
   Y : T;
   X := X + Y;
end Ttada;

Strong vs Weak Typing

The key attribute of strongly typed languages is that variables, constants, etc can be used only in manners consistent with their types. In contrast weakly typed languages offer many ways to bypass the type system.

A good comparison would be original C vs (Ada or Scheme). C has unions, varargs, and a deliberate confusion between pointers and arrays. Original C permitted many other type mismatches. A motto for C is "Trust the programmer!". Both Ada and Scheme are much tighter in this regard: both are strongly typed, Ada is (mostly) statically typed.

Compare the three programs on the right. The C program compiles and runs without errors! The Scheme define is accepted, but (test) gives an error report. The Ada program doesn't even compile.

Static vs Dynamic (Strong) Type Systems

Static and Dynamic strongly typed systems both prevent type clashes, but the prevention is done at different times and by different methods.

In a static type system

In a dynamic type system

Ada, Pascal, and ML have static type systems.

Scheme (Lisp), Smalltalk, and scripting languages (if strongly typed) have dynamic type systems. These systems typically have late binding as well.

A mixture is possible as well. Ada has a very few run-time checks; Java a few more.

Static type systems have the following advantages.

Dynamic type systems have the following advantages.

7.1.2: Polymorphism

Definition: Polymorphism enables a single piece of code to work with objects of multiple types.

Definition: In parametric polymorphism the type acts as though it is an extra unspecified parameter to every operation.

Consider dynamic typing as use in Scheme. Depending on the type of the operands, the addition operator + can indicate, integer arithmetic, real arithmetic, or infinite precision arithmetic. If the operands are of inappropriate type, + signals an error. Since the type was never specified by the programmer, this is often called implicit parametric polymorphism.

fun len xs =
  if null xs
  then 0
  else 1 + len (tl xs)

The example on the right is written ML, which is statically typed, yet still manages to support implicit parametric polymorphism. The (very slick) idea is that ML supports type inference so is able to deduce individual types from the type of an expression. In this case the interpreter determines that the type of the len function as 'a list→int, i.e., a function with parameter a list of type 'a (unknown) and result integer.

Consider instead generics in Ada and Java and templates in C++. In this case the programmer writes code for each type and system chooses which one to invoke depending on the type. This is called explicit parametric polymorphism.

We will soon learn that the positive integers can be considered a subtype of the integers and that a value in a subtype can be considered a value on the supertype. This is called subtype polymorphism.

Similarly, the ability to consider a class as one of its superclasses is called class polymorphism.

7.1.3: The Meaning of Type

Types can be though of in at least three ways, which we will briefly describe. They are the denotational, constructive, and abstraction-based viewpoints.

With denotational semantics:

  1. A type is simply a set T of values.
  2. A value has type T if it belongs to the set.
  3. An object has type T if it is guaranteed to be bound to a value in T.

An advantage of denotational semantics is that composite types, e.g., arrays and records, can be described using standard mathematical operations on sets.

With constructive semantics:

  1. A type is either built-in or
  2. Constructed from basic-in or other constructed type using a type-constructor (record, array, etc).

With abstraction-based semantics, a type is an interface consisting of a set of operations with well defined, consistent semantics. It is characteristic of object-oriented languages.

In practice, we normally think of a type using all three viewpoints.

7.1.4: Classification of Types

We will first discuss several scalar types and then composite types that consist of aggregates of both scalar and other composite types.

Scalar Types

Discrete Types

The term discrete comes from mathematics, where it is contrasted with continuous.

A type is considered discrete if there is a clear notation of successor and predecessor for values in the type. Mathematically, this gives is an isomorphism between the elements of the type and a consecutive subset of integers. Indeed, the basic examples of discrete types are the integers themselves and enumeration types.

Integers. Of course with only a finite number of bits to use for the representation (most commonly 16, 32, or 64), the integer type is really just a finite subset of the mathematical integers.

  type Suit  is (Club, Diamond, Heart, Spade);
  type Organ is (Lung, Heart, Brain, Skin, Liver);
  Card : Suit  := Heart;  -- Legal
  Sick : Organ := Heart;  -- Legal
  Sick := Card;           -- Compile time error

Enumeration Types. These types have an obvious and compact implementation: Values in the type are mapped to consecutive integers.

  type Score is new Integer range 0..100;
  type Day   is (Mon, Tue, Wed, Thu, Fri, Sat, Sun);
  subtype Weekday is Day range Mon..Fri;
  X  : Integer := 3;
  Y  : Score   := 3;
  D1 : Day     := Sun;
  D2 : Weekday := Mon;
  if D1 < D2 then   -- legal
    X := Y;            -- illegal
  end if;

Subrange Types. Ada and Pascal support types that are subsets of others; Ada has two quite useful variations. The type Score is another type. It happens to have a subset of the values and the same operations as does the type integer, but a Score is definitely not an integer: The assignment of Y to X on the right is a compile-time error.

In contrast Weekday is not a new type but instead a subtype of Day. Hence values of the two types can be combined and assignment from one to the other is legal. However, a (often run-time) check is needed if a Day is assigned to a Weekday to ensure that the constraint is satisfied.

Other Numeric (Scalar) Types

Nearly all languages have several other numeric types.

Non-numeric Scalar Types

We consider here Boolean, character and string, and void.

Boolean. The type was named after George Boole and is very common. C came late to the party: Boolean was added only in C99.

Character and Strings. Very common (exception: javascript has no character type). An important modern consideration is support for non-ascii characters. Most modern languages support at least a 16-bit-wide character. As an example of the growing importance of enhanced character types, I note that Ada83 had only (8-bit) character, Ada95 added 16-bit wide_characters, and Ada05 added in addition 32-bit wide_wide_characters. In each instance there is a string type holding the corresponding characters.

Another question is whether strings can be changed during execution or are they instead only constants. Java chose the latter approach, most other languages the former.

Finally we come to void, which is used as a return type of a procedure in C and Java to indicate that no value is returned. ML has a similar type unit type, which has only one value written (). Haskell has the same type but () names both the type and the only value of the type.

Composite Types

Non-scalar types are normally called composite and are generally created by applying a type constructor to one or more simpler types. We will study several composite types shortly. Here we just list a bunch of them with very brief commentary.

  type Univ is record
    Name : string (1..5);  -- fancier strings exist
    Zip  : integer;
  end record;
  NYU : Univ;
  A : array (1..5) of integer
  NYU := ("NYU  ", 10021);          -- positional
  NYU := (Zip=>10021, name=>"NYU"); -- named
  A := (5, 4, 3, 2, 1);
  A := (1..3=>5, 5=>2, 4=>3);

7.1.5: Orthogonality

Languages try to have their features orthogonal, i.e., the rules for one feature apply to all possibilities of another. This is as opposed to have special rules for all situations. Original Fortran required array bounds to be integers, Pascal, Ada, et al. permit any discrete type; Early C requires array bounds to be know at compile time, but modern C permits the bound to be a parameter; Ada requires the bound to be known at the time the array declaration is elaborated.

An important example of orthogonality is whether the language permits literals to be written for composite types. Such literals are called aggregates. An Ada example is on the right.

7.1.A: Assigning Types

How does the programmer and/or system specify the type of a program construct? At least three methods exist.

  1. Explicit type declarations in the program. This is by far the most common.
  2. No compile-time bindings. This is for dynamically-typed languages like Scheme
  3. The syntax of the construct determines its type. In Fortran variables beginning with I-N were by default Integer; others were by default Real. I don't believe any new languages do this.

Homework: CYU 1, 2, 3, 4, 10.

7.2: Type Checking

The 3e terminology is not standard and many do not use it. The 3e uses type inference to include both the case where the type of a composite is determined by the types of the constituents, and the opposite case where the type of a constituent is determined from its context and the type of the composite.

I believe normally type inference is just used for the second case of determining the type of a constituent from its context. The first case (constituent to composite) is then called type synthesis.

For type synthesis the programmer declares types of variables and the compiler deduces the types of expressions and determines if the usage is permitted by the type system.

For type inference (e.g., ML and Haskell) the programmer does not declare the type of variables. Instead the compiler deduces the types of variables from how they are used. For a trivial example an occurrence of X+1 implies that X is an integer.

  type T1 is new integer;
  type T2 is new integer;
  type T3 is record x:integer; y:integer; end record;
  type T4 is record x:integer; y:integer; end record;
  subtype S1 is integer;

7.2.1: Type Equivalence

When are two types equivalent? There are two schools: name equivalence and structural equivalence. In (strict) name equivalence two type definitions are always distinct; Thus the four types on the right T1,...,T4 are all distinct. In structural equivalence, types are equivalent if they have the same structure so types T3 and T4 are equivalent and aggregates of those two types could be assigned to each other. Similarly, T1, T2, and integer are equivalent under structural equivalence.

Ada uses name equivalence so the types are distinct. However, Ada offers subtypes, which are compatible (see 7.2.2) to the parent type (but can, and often do, have range constraints). So S1 is equivalent to integer.

  type student = {
    name:    string,
    address: string }
  type school = {
    name:    string,
    address: string }

Most new languages, but not ML from which the example on the right is taken, adopt name equivalence to avoid have student and school considered equivalent types. Assigning a student to a school is normally a sign of a bug that should be caught as early as possible.

Many languages have a mixture of name and structural equivalence. For example in C structs use name equivalence; whereas structural equivalence is used for everything else.

Variants of Name Equivalence

In addition to strict name equivalence as used above, there is also a concept of loose name equivalence where one type can be considered an alias of another. For example in Modula-2, which has loose name equivalence,

would be considered an alias of INTEGER and variables of type T5 could be assigned to variables of type INTEGER.

Type Conversions and Casts (and Nonconverting Type Casts)

What happens if type A is needed and we have a value of type B? For example, if we have an assignment statement X:=Y with Y of type A and X of type B. For another example, suppose we invoke F(Y) with Y of type A and the parameter of F of type B. In these cases we need to convert the value of type A to a value of type B. In many languages, the programmer will need to indicated that the conversion is desired. For example in Ada, assuming X is of type T1 above and Y of type T2, the programmer would write

    X := T1(Y);
Consider four cases.
  1. Types A and B are structurally equivalent, but the compiler uses name equivalence. The programmer must state that the conversion is desired, but no code is generated since the types use the same implementation.
  2. The types are different, but for some values share the same representation. In this case only a check is needed. If the check passes (or can be deduced at compile time), the assignment is done without conversion.
  3. If the representation is different, both checking and conversion code may be needed. For example assigning an integer to a floating point variable requires conversion and the reverse assignment requires a range check as well as conversion (also the conversion may involve loss of precision).
  4. A nonconverting type cast is an assertion by the programmer that the system should just treat the value of type A as a value of type B. This is quite dangerous, but is sometimes needed in low-level code. For example, you read some bits from the network and then subsequent bits tell how to interpret the previous bits. This is called an unchecked_conversion in Ada and a reinterpret_cast in C++.

Homework: CYU 13, 14.

Homework: 1, 2, 3, 6.

Start Lecture #7

Remark: Midterm exam next recitation.
Homework assigned today is due the beginning of next class not next recitation.

Remark: Recall from last lecture that a type system consists of:

7.2.2; Type Compatibility

A value must have a type compatible with the context in which the value is used. For most languages this notion is significantly weaker than equivalence; that is, there may be several non-equivalent types that can legally occur at a given context.

We first need to ask what are the contexts in which only a type compatible with the expected type can be used. Three important contexts are

As these examples indicate, there is often a natural type for a context and the question is whether the type that actually appears is compatible with this natural type. Thus the question reduces from is type T compatible at this context? to Is type T compatible with type S?.

The definition of type compatibility varies greatly from one language to another. Ada is quite strict. Two Ada types S and T are compatible in only three cases.

Pascal is slightly more lenient; it permits a integer to appear where a real number is expected. Note that permitting an integer to stand for a real implies that a type conversion will be performed under the covers.

Fortran and C are more liberal and will perform more automatic type conversions.


Such implicit, or automatic type conversions are called coercions.

Widespread coercions are a controversial feature of C and other languages since they significantly weaken the type system.

Note that in coercing a value of type T into a value of type S, the system may need to

  1. Check if the value meets a constraint.
    For example, an integer must be ≥0 in order to be considered a positive).

  2. Convert the low-level representation of the value.
    For example, a 32-bit signed integer (type T) must have its representation changed significantly when coerced into an IEEE-standard, double-precision floating point number (type S).

    In this example no constraint checking is needed and no precision can be lost since every 32-bit signed integer has a (unique) representation as a (normalized) value in S.

    The reverse coercion (S to T) is more involved. Most floats are too big in absolute value to be represented in T and most that do fit lose precision in the process.

A language like Ada never performs a coercion of the second kind (types T and S that would require this kind of conversion are not compatible). Instead, the programmer must explicitly request such conversions explicitly by using the name of the target type as a function. For example Y:=float(3); would be used if Y is of type float.

In contrast C permits the above without the float() and even permits a naked x=3.5; when x is an int.

A recent trend is to have less coercions and thus stronger types. In this regard, consider the C++ feature permitting user-defined coercions. Specifically, C++ permits a class definition to give coercion operations to/from other types. Should this be called a coercion at all? That is, are user-defined coercions really automatic?

Overloading and Coercion

Definition: Overloading occurs when there are multiple definitions for the same name that are distinguished by their type.

Definition: The decision which of these definitions applies at a given use of the name is called overload resolution.

Normally, overloading is used only in static type systems and the overloaded name is that of a function. In resolving an overloaded function name uses the signature of each of the individual functions, i.e.,

Overloading is related to coercion. Instead of saying that the int in int+real is coerced to a float, we define another addition operator named + that takes (int,float) and produces float. Naturally, this operator works by first converting the int to a float and then does a floating point addition. This proposal is not practical when there are many types and/or many parameters. For example sum4(a,b,c,d) would need 34=91 definitions if it is to be work when each argument could be integer, positive, or natural.

int main (void) {
  int f(int x);
  int f(char c);
  return f(6.2);
int f(int x) {
  return 10;
int f(char c) {
  return 20;

Alternatively, one could eliminate all overloading of addition by just defining float+float and coercing int+int in addition to int+float and float+int.

The combination of overloading and coercion can lead to ambiguity as shown by the C++ example on the right. The g++ compiler gives the following remarkably clear error msg when handed this program.

tt.c: In function 'int main()':
tt.c:4: error: call of overloaded 'f(double)' is ambiguous
tt.c:2: note: candidates are: int f(int)
tt.c:3: note:                 int f(char)
The problem is that a C/C++ double (such as 6.2) can be coerced into either int or char. This same ambiguity would occur if, instead of 6.2, we used a variable of type double.

Literals   Either literals must be considered to be of many types (e.g., 6 would be an integer, a positive, a natural, and a member of any user types derived from integer) or literals must be members of a special type universal integer that is compatible with any of the above types. Ada uses this second solution.

Universal Reference Types

Several languages provide a universal reference type (also called a generic reference type). Elements of this type can contain references to any other type. In C/C++ the universal reference type is void * and can be thought of as a pointer to anything.

In an object-oriented language the analogous type is object in C# and Object in Java. Since objects are normally heap allocated, one could consider object and Object to also be pointers to anything, or references of anything.

It is type safe to put any reference into an object of universal reference type: Since the type of an object referred to by a universal reference is unknown, the compiler will not allow any operations to be performed on that object. For example, gcc complains about the pair of C statements

    void *x;     strlen(x);

However the reverse assignment in which a universal reference is assigned to a more specific reference type is problematic since now operations appropriate to this specific reference can be applied to a value that belongs to the general reference.

A Java-like example would go as follows. The standard java.util library contains a Stack container class. A programmer uses the constructor and obtains stack a member of the class. Any object can be pushed onto stack since it is a stack of Object's. The programmer pushes a rectangle and a parallelogram onto the stack. This is fine. Members of the stack are Objects and any operation applicable to an Object can be legally applied to a parallelogram or to a rectangle. Now the programmer wants to pop the stack and have the top member assigned to a rectangle. Java will not permit this since the member is an Object and objects cannot be assigned to rectangles. The programmer must use a cast when doing the pop, telling the system that the top member is a rectangle. Java actually keeps tags with objects so can verify that the top Object is actually a rectangle. If it is in fact only a parallelogram, a run time error is reported. This is dynamic type checking. If the top member of the stack was a rectangle and the programmer tried to pop it onto a parallelogram object, Java would detect that the top stack member belongs to a subclass of rectangle so no error occurs. Type safety has been preserved.

Summary: In object oriented terminology x=y; is safe if y is more specific than x (i.e., y has more operations defined). In contrast y=x; cannot be permitted without checking that the value in x is suitable for y.

The real problems arise with a language like C (and void *) since no tags are kept and the system cannot determine whether or not the pointer being popped does reference a rectangle or parallelogram.

Hence the programmer must use an unchecked type conversion between
   void * and (struct rectangle) *.
Type safety has been lost.

7.2.3: Type Inference/Synthesis

In this section we emphasize type synthesis, i.e, determining the type of an expression given the type of its constituents. Next lecture, when we study ML, we will encounter type inference, where the type of the constituents in determined by the type of the expression.

Easy Cases

Sometimes it is easy to synthesize the type of the expression. For example,

with Ada.Integer_Text_IO; use Ada.Integer_Text_IO;
procedure Ttada is
  subtype T1 is Integer range  0..20;
  subtype T2 is Integer range 10..20;
  A1 : T1;
  A2 : T2;
  Get (A1); Get (A2);
  Put (A1+A2);
end Ttada;


What would be the type of A1+A2 in the Ada program on the right? Two possibilities come to mind.

Ada chooses the first approach. Indeed, in Ada values are associated with types, not subtypes. It is variables that can be associated with subtypes. As a result in Ada, assigning to a subtype that has a range constraint may require a run-time check.

This is also true of types with range constraints, but our use of Ada will only add ranges to subtype definitions, not type definitions

Composite Types

7.2.4: The ML Type System

Next lecture will be on ML, including the type system.

Homework: CYU 15, 16, 18

7.3: Records (Structures) and Variants (Unions)

Records are a common feature in modern programming languages; however the terminology differs among the languages. They are referred to as records in Pascal and Ada, just types in ML and Fortran, and structs (short for structures) in C. They are similar to methodless classes in Java, C#, C++.

In all cases a record is a set of typed fields. Some choices to be made in defining records for a specific language.

type    atomic_element is record
  Name          : String (1..2);
  Atomic_Number : Integer;
  Atomic_Weight : Float;
end record;
struct atomic_element {
  char   name [2];
  int    atomic_number;
  double atomic_weight; };
type atomic_element = {
  name          : string,
  atomic_number : int,
  atomic_weight : real};

7.3.1: Syntax and Operations:

On the right are specifications of the same record type in three languages, Ada, C, and ML. Note that in ML there is no , after real. Like Algol this ML punctuation is a separator not a terminator.

If you want to play with ML, the command name is sml, which is short for standard ML of New Jersey. As noted above the order of the fields in a record is not significant in ML. It appears to me that sml alphabetizes the fields; that is, the example is reordered to atomic_weight, atomic_number, name.

Records in Scheme are somewhat more complicated; we won't use them.

7.3.2: Memory Layout and Its Impact

The issue here concerns alignment of fields within a record. Today essentially all computer architectures are byte addressable and hence, in principle, each field could start at the byte right after the last byte of a the previous field. However, architectures normally have alignment requirements something like a datatype requiring n-bytes to store must begin at an address that is a multiple of n. For example, a 4-byte quantity (e.g., an int in C) cannot occupy bytes 10,002-10,005.

In most cases the truth is that an int in C could occupy any 4 consecutive bytes, but accessing the int would be significantly faster if it was properly aligned so that its starting address is a multiple of 4. We will not be so subtle and will simply use the crude approximation given by the quote in the previous paragraph.

Note that this issue is not so severe with arrays as with records. Since arrays are homogeneous, if the first element is aligned and the elements are packed with no padding in between, then all elements will be aligned. With records, such as atomic_element it is harder. Assume the record itself is aligned on a doubleword (8-byte) boundary. That means that the name field will begin on an address like 8800 that is evenly divisible by 8. However, the atomic_number field of the record will begin 2-bytes later and hence will not be properly aligned for integer datatype (which we assume is 4-bytes in size).

Hence atomic_number is not properly aligned and cannot be accessed efficiently. As a result a two-byte pad is inserted between name and atomic_number. This wastes space. Assume that the float/real/double value atomic_weight needs 8-byte alignment. This alignment is satisfied since name + pad + atomic_number consume 8 bytes and the record itself is 8-byte aligned. Thus the total padding required is 2-bytes which is 1/8 of the total space of the padded record.

Now note that if the record was reordered so the fields were weight, number, name (i.e. decreasing size and required alignment), there would be no padding at all. ML changes the ordering visibly, but does not use size as the ordering criterion. Some languages permit the compiler to reorder. Since arbitrary reordering is impossible for systems programming, where the fields can correspond to specific hardware device registers whose relative addresses are given, systems programmers must choose languages in which such reorderings can be overridden.

One last point. If the padding is garbage (i.e., nothing specific is placed in the pad), then comparing a record requires comparing the fields separately since a single large comparison would require that the two garbage pads be equal. Thus another trade-off occurs. Padding with garbage makes creating a record faster, but padding with zeros (or any other constant) makes comparing two records faster.

Homework: 8.

7.3.3: with Statements

7.3.4: Variant Records (Unions)

Definition: A variant record is a record in which the fields act as alternatives, only one of which is valid at any given time. Each of the fields is itself called a variant.

with Ada.Integer_Text_IO; use Ada.Integer_Text_IO;
procedure Ttada is
  type Point is record
    XCoord : Float;
    YCoord : Float;
  end record;
  type ColorType  is (Blue, Green, Red, Brown);
  type FigureKind is (Circle, Square, Line);
  type Figure(Kind : FigureKind := Square) is record
    Color   : Colortype;
    Visible : Boolean;
    case Kind is         -- Kind is the discriminant
      when Line   => Length      : Float;
                     Orientation : Float;
                     Start       : Point;
      when Square => LowerLeft   : Point;
                     UpperRight  : Point;
      when Circle => Radius      : Float;
                     Center      : Point;
    end case;
  end record;
  C1 : Figure(Circle);   -- Discrimant cannot change
  S1 : Figure;   -- Default discriminant, can change
  function Area (F : Figure) return Float is
    case F.Kind is
      when Circle => return 3.14*F.Radius**2;
      when Square => return F.Radius;       -- ERROR
      when Line   => return 0.0;
    end case;
  end Area;
  S1 := (Square, Red, True, (1.5, 5.5), (1.2, 3.4));
  C1.Radius := 15.0;
  if S1.LowerLeft = C1.Center then null; end if;
  -- ERROR to write S1.Kind := Line;
  -- ERROR to write C1 := S1;
end Ttada;

Actually a variant can be a set of fields. For example the Line variant on the right has three fields. As is also shown in the example a record Figure can have several non-variant fields as well as several variant fields.

In some languages (e.g., Ada, Pascal) an explicit field, called the discriminant or tag keeps track of which variant is currently valid. In the example on the right the discriminant is Kind. We will see soon that Scheme employs an implicit discriminant.

Definition: When the variant record contains an explicit or implicit discriminant, the record is called a discriminated union. When no such tag is present, the variant record is called a nondiscriminated union..


Clearly languages with nondiscriminated unions, e.g. C's union construct or Fortran's equivalence, give up type safety since it is the programmer who must ensure that the variant read is the last variant that was written. However, even languages like Pascal and Modula-2, which have discriminated unions, have type weakness in this area. To see the technical details read section 7.3.4 of the 3e (that section is one of those on the CD).

Ada is fairly type safe, but it is complicated. The Modula-3 designers did not like the type dangers in Modula-2 and the complexity of Ada and omitted variant records entirely.

Variants in Ada

The Ada example illustrates a number of features of the language; here we concentrate on just the variant record aspects. Figure is a variant record with Kind the discriminant. Note that the discriminant is given a default value; that is what permits us to define S1 without giving a discriminant as we did for C1. If Kind did not have a default then the declaration of S1 would be an error.

C1 will always be a circle; see the ERROR comment all the end. S1 can be any figure. Note, however, that you can only change the tag when you change the entire variant record; see the ERROR comment near the end.

Although this program compiles, it is incorrect. The Area function checks the Kind of its argument, but, if the Kind is Square, it references the Radius component. If Area were actually called with a Square, a run-time error would occur.

Finally, null is an Ada statement that does nothing. I used it here to make the if statement legal (although useless). In reality there would be other code in the then arm.

(define X 35)
(+ X 5)
(define X '(a 4))
(+ X 5)

Discriminated Unions with Dynamic Typing

Recall that Scheme has strong, but dynamic typing. That means, that the variables are not typed but the values are. If you present the example on the right to a Scheme interpreter, you will get an error for the last line. X was first an integer; then a list. Although there is no explicit tag, the system keeps one implicitly and thus can tell that the last line is a type error since you can't add an integer to a list.

The Object-Oriented Alternative

Instead of the type-unsafe unions of C, a Java programmer could have a base class of the non-varying components and then extend it to several classes one with each arm of the union.

7.4: Arrays

Definition: An array is a mapping from an index type to an element type (or component type).

Arrays are found in essentially all languages. Most languages require the index type to be an integral type. Pascal, Ada, and Haskell allow the index type to be any discrete type.

  Ada:    (82, 21, 5)
  Scheme: #(82, 21, 5)

First-Classness: In C/C++ functions cannot return arrays and there are no array literals (but there are array initializers). Scheme and Ada have array literals as shown on the right

7.4.1: Syntax and Operations

The syntax of array usage varies little across languages. The one distinction is whether to use [] or () for the subscript. Most common is [], which permits array usage to stand out in the program. Fortran uses () since Fortran predates the widespread availability of [] (the 026 keypunch didn't have []). Ada uses () since the designers wanted to emphasize the close relation between subscripted references and function evaluations. A(I) can be thought of as the Ith component of the array A or the value of the function A at argument I. Since an array is a function from the index type, there is merit to this argument.

  float x[50];
  type Idx is new Integer range 0..49;
  X : array (Idx) of Float;
  Y : array (Integer range 0..49) of Float;
  Z : array (0..49) of Float;
  type Vec is array (0..9) of Float;
  Mat1 : array (0..9) of Vec;
  Mat1(3)(4) := 5.0;
  Mat2 : array (0..9, 0..9) of Float;
  Mat2(3,4) := 5.0;


An array declaration needs to specify the index type and the element type. For a language like C, the index type is always the integers from zero up to some bound. Thus all that is needed is to specify the component type and the upper bound as shown on the right.

With the increased flexibility of Ada arrays comes the requirement to specify additional information, namely the full index type. The first two lines, give us 50 Floats, just like the C example, but the index type is idx so you can't write X(I) where I is an integer variable. You can write Y(I) and Z(I) which are of the same type (the second declaration is an abbreviation of the first.

Multi-Dimensional Arrays Essentially all languages support multidimensional arrays; the question is whether a two-dimensional (2D) array is just a 1D array of 1D arrays. Ada offers both possibilities as shown on the right. The 2D array Mat2 has elements referenced as Mat2(2,3); whereas the 1D array of 1D arrays Mat1 has elements referenced as Mat1(2)(3). The 2D notation is less clumsy, but slices (see below) are possible only with Mat1. C arrays, are like Mat1: they are 1D arrays of 1D arrays. However C does not support slices.

Slices and Array Operations

A slice or section is a rectangular section of an array. Fortran 90 and many scripting languages provide extensive facilities for slicing. Ada provides only 1D support. Z(11..44) is a slice of floats and Mat1(2..6) is a slice of Vec's. Note that this second example is still a 1D slice. You cannot get a general 2D rectangular subset of the 100 floats in Mat1.

7.4.2: Dimensions, Bounds, and Allocation

Definition: The shape of an array consists of the number and bounds of each dimension.

Definition: A dope vector contains the shape information of the array. It is needed during compile time (not our concern) and for the most flexible arrays is needed during run-time as well.

The binding time of the shape as well as the lifetime of the array determines the storage policy used for the array. The trade-off is between flexibility and run-time efficiency.

7.4.3: Memory Layout

The layout for 1D arrays is clear: Align the first element and store all the succeeding elements contiguously (they are then guaranteed to be aligned).

We will discuss 2D arrays; higher dimensional arrays are treated analogously. Assume we have an array A with 6 rows (numbered 0..5) and 3 columns. Clearly A[0][0] will be stored first (and aligned). The big question is what is stored next.

The reason the layout is important is that if an array is traversed in an order different from how it is laid out, the execution time increases, perhaps dramatically (cache misses, page faults, stride prediction errors).

Alignment and Memory Usage

Alignment is not a major issue for most arrays; align the first element and store the rest contiguously. The exception is arrays of records. Imagine a record consisting of a 1-byte character and an 8-byte real. The total size of the fields is 9-bytes. If the byte is stored first and the record itself is 8-byte aligned (as it would be in most languages) then 7 bytes of padding are needed before the real. Thus the record size would be 16 bytes and an array of 100 such records would require 1600 bytes, of which only 900 bytes are data.

If the real is stored first, the character is aligned without padding and the record takes 9 bytes, but the next record in the array needs 7 bytes of padding so a 100-element array of these records takes 1593 bytes (there is no padding needed after the last record).

If, instead of an array of records, the data structure was organized as a record of arrays (an array of characters followed by an array of reals), the memory efficiency is better. If the reals are stored first, no padding is needed so the space required is 900 bytes. If the characters are stored first, the array of reals will need a 4-byte pad (since 100/8 leaves a remainder of 4) and the total space required is 904 bytes.

Homework: Consider the following C definitions.

    struct s1 {
      char   c1;
      double d1;
      char   c2;
      double d2;
    } A[100];
    struct s2 {
      char   c3[100];
      double d3[100];
      char   c4[100];
      double d4[100];
    } B;
Assume char requires 1 byte and double requires 8 bytes and that a datatype must be aligned on a multiple of its size.
  1. How much actual data (not counting padding) is occupied by all of A? by all of B?
  2. How large is A and B including the padding?
  3. Reorder the fields of A and B to minimize padding? How much space did you save.

7.5: Strings

7.6: Sets

7.7: Pointers and Recursive Types

An important distinction that must be made before looking at pointers and recursive types is the value model of variables vs the reference model.

In the value model, used by C, Pascal, and Ada, a variable such as X denotes its value, say 12. In the reference model, used by Scheme and ML, X denotes a reference to 12 (in a coarse sense X points to 12). You might think that with the reference model, expressions would be difficult, but that is not so. When you write X+1 the reference X automatically de-referenced to the value 12.

Java uses the value model for built-in scalar types and uses the reference model for user-defined types.

7.7.1: Syntax and Operations

Turning to recursive types, consider two very common linked data structures: linked lists and trees. Take a trivial example, where a tree node is one character and two pointers (to the left and right subtrees; we are considering only binary trees). So a node is a record with three fields: a character, and two references to child node's.

Reference Model

In ML we would define a tree node tnode as

    datatype tnode = empty | node of char * tnode * tnode;
The | indicates an alternative. The empty is used for a leaf (we are assuming the data of the tree is the characters stored in the non-leafs). The interesting part is after the |. A tnode is a tuple consisting of a character and two tnode's. But that is not possible! It is really a character and two references to tnode's. But ML uses the reference model so writing tnode means a reference to tnode.

procedure Ttada is
  type Tnode;        -- an incomplete type
  type TnodePtr is access Tnode;
  type Tnode is record
    C : Character;
    L : TnodePtr;
    R : TnodePtr;
  end record;
  T : TnodePtr := new Tnode'('A',null,null);
  T.L := new Tnode'('L',null,null);
  T.R := new Tnode'('R',null,null);
  T.all.R.all.C := 'N';
  T.R.C := 'N';
end Ttada;
struct tnode {
  char  c;
  tnode *left;
  tnode *right;
struct tnode *t;

Value Model

With the value model, we need something to get a reference to a tnode. The Ada code is on the right. The first line declares (but doesn't define) the incomplete type Tnode. The next line defines the type reference to Tnode. The key is that an access definition does not need the entire definition of the target. Then we can define Tnode and build a simple tree with a root and two leaves for children. A tree is just a pointer to the root.

The C++ structure definition is shorter since you can mention the type being defined within the definition. However, when two different types contain references to each other, the C++ code uses the same idea of incomplete declaration followed by complete definition that Ada does.

Pointers and Dereferencing: Looking again at the Ada example, T is a reference to a tree-node. That is nice, but we also want to refer to the tree-node itself and to its components. That is we want to dereference T. For this we use .all and write T.all, which is a record containing three fields.

As with any record, we write .L to refer to the field named L and so T.all.L is the left component of the root. Similarly T.all.R is the right component and T.all.R.all.C:='N' changes the character field of the right child to 'N'. The construct .all.fieldName occurs often and can be abbreviated to .fieldName as shown.

In C/C++ the dereferencing is done by *. Thus t points to a tree-node *t is the tree node and (*t).c is the character component. You can abbreviate *(). by -> and write t->c.

  void f (int *p) {...}
  int a[10];
  f(a); f(&a[0]); f(&(a[0]));
  int *p = new int[4];
  ... p[0]   ...
  ... *p     ...
  ... p[1]   ...
  ... *(p+1) ...
  ... p[10]  ...
  ... *(p+9) ...

Pointers and Arrays in C

In C/C++ pointers and arrays are closely related. Specifically, using an array name often is the same as using a pointer to the first element of the array. For example, the three function calls on the right are all the same. Similarly the references involving p are the same for both members of each pair. Also note that the last pair reference past the end of the array; they are an undetected errors.

There are, however, places where pointers and arrays are different in C, specifically in definitions.
Consider   int x[100], *y;   Although both x and y point to integers, the first definition allocates 100 (probably 4-byte) integers, while the second allocated one (probably 4-byte or 8-byte) pointer.

Dangers with Pointers

It is quite easy to get into trouble with pointers if low-level manipulation is permitted. For example.

7.8: Lists (and Sets and Maps)

First we need to define these three related concepts.

Definition: A list is an ordered collection of elements.

Definition: A set is a collection of elements without duplicates and with fast searching.

Definition:A map is a collection of (key,value) pairs with fast key lookup.

Normally, only very high level languages have these built in. They are often in standard libraries and can be programmed in essentially all languages. Specifically,

7.A: Procedures and Types

Procedure Types

Assigning types to procedures is needed if procedures can be passed as arguments or returned by functions. A statically typed language like Ada does need the signature of procedures so that it can type check calls. However, it does not need to assign a type to a procedure itself since no procedure can be passed as an argument. Thus you can't say in Ada something like
type proc_type is procedure (x : integer);


As noted above statically typed languages like Ada require procedures to be declared with full signatures. C supports procedures with varying numbers of arguments, which is a loophole in the C type system. Java supports a limited version of varargs that is type safe. Specifically all the optional arguments must be of the same (declared) type.

7.9: Files and Input/Output

7.10: Equality Testing and Assignment

Start Lecture #8

Remark: Lab2 has not yet been assigned. The first question will be to redo lab1 in ML.

7.2.4: (ML and) The ML Type System

This section uses material from Ernie Davis's web page in addition to the sources previously mentioned.

Overview of ML

ML was developed by Robin Milner et al. in the late 1970's. It was originally developed for writing theorem provers. We will be most interested in its type system, especially in its ability to use type inference so that we get static typing even though the programmer need very few type declarations. Instead the ML system deduces the types from the context!


We will be using Standard ML of New Jersey, which you invoke by typing sml. ML has the following characteristics

- 7+5 ;
val it = 12 : int
- val x = 7+5;
val x = 12 : int

- 7 = + = 5; val x=12 : int
- 2.0+3.5; val it = 5.5 : real
- 2 + 3.5; stdIn:4.1-10.7 Error: operator and operand don't agree [literal] operator domain: int * int operand: int * real in expression: 2 + 3.5

Some Simple Examples

As with Scheme, you can use ML as a desk calculator. You notice right away that the notation is conventional infix. Other simple characteristics include.

- [2,3,4,5];
val it = [2,3,4,5] : int list

- [[2.0,3.2], [], [0.0]]; val it = [[2.0,3.2],[],[0.0]] : real list list
- [2.2,5]; stdIn:8.4-9.8 Error: operator and operand don't agree [literal] operator domain: real * real list operand: real * int list in expression: 2.2 :: 5 :: nil
- [1,[2,4]]; stdIn:1.1-4.2 Error: operator and operand don't agree [literal] operator domain: int * int list operand: int * int list list in expression: 1 :: (2 :: 4 :: nil) :: nil
- 2::[3,5]; val it = [2,3,5] : int list
- hd([2,3,5])); val it = 2 : int;
- tl([2,3,4]); val it = [3,4] : int list
- [2,3]@[5,7]; val it = [2,3,5,7] : int list
- []; val it = [] : 'a list


We will study lists, tuples, and records, emphasizing the first two.

Lists: As in Scheme lists will prove to be important.

- val x = (2, 7.5, "Bob");
val x = (2,7.5,"Bob") : int * real * string

#2 x; val it = 7.5 : real

Tuples: Tuples are of fixed length (no concatenation), and can have heterogeneous elements. They are written with parentheses and their elements are selected by index.

Tuples will also prove to be important. Formally, all procedures take only one parameter, but that parameter can be a tuple and hence it is easy to fake multiple parameters.

- val bob = { age=32, firstName="Robert", lastName="Jones", weight=183.2};
val bob = {age=32,firstName="Robert",lastName="Jones",weight=183.2}
  : {age:int, firstName:string, lastName:string, weight:real}
- #age bob; val it = 32 : int

Records: Records are tuples with named fields. Records are written with braces and elements are selected by giving the field name.

- if (1 < 2) then 3 else 4;
val it = 3 : int
- if (1 < 2) then 3 else 4.0; stdIn:38.26-39.26 Error: types of if branches do not agree [literal] then branch: int else branch: real in expression: if 1 < 2 then 3 else 4.0


Naturally some kind of if-then-else is provided; in ML it is actually if-then-else.

Note that the value of the then arm must have the same type as the value of the else arm. Again this is important for type inference.

- fun add3 I = I+3;
val add3 = fn : int -> int
- add3 7;
val it = 10 : int
- add3 (7);
val it = 10 : int
- fun times(X,Y) = X*Y; val times = fn : int * int -> int - times(2,3); val it = 6 : int
- val t = (2,3); val t = (2,3) : int * int - times t; val it = 6 : int
- fun sum3(x,y,z) = x+y+z; val sum3 = fn : int * int * int -> int - sum3(5,6,7); val it = 18 : int


Functions are of course crucial since ML is a functional language.

It is important to note the type of each function on the right and see that ML can determine it without user intervention.

All ML functions take only one argument. Functions of multiple arguments are actually functions of the corresponding tuple. The tuple can be written with () as in the (2,3) example or a tuple variable can be defined as we did as well.

Although not shown, a function can also return a tuple.

Note that ML responds to a fun definition with a val and a fn. As mentioned before val is akin to (define and now note that fn is akin to (lambda.

- fun sumTo(N) = if (N=0) then 0 else N+sumTo(N-1);
val sumTo = fn : int -> int
- sumTo 10;
val it = 55 : int
fun member(X,L) = if (L=[]) then false else if hd(L)=X then true else member(X,tl(L)); stdIn:49.24 Warning: calling polyEqual stdIn:49.54 Warning: calling polyEqual val member = fn : ''a * ''a list -> bool - member(5, [1,5,3]); val it = true : bool
- fun nth(I,L) = if (I=1) then hd(L) else nth(I-1,tl(L)); val nth = fn : int * 'a list -> 'a - nth (3, ["When", "in", "the", "course"]); val it = "the" : string
fun f(X) = if X <= 2 then 1 else g(X-1) + g(X-2) and g(X) = if X <= 3 then 1 else f(X-1) + f(X-3); - f(20); val it = 2361 : int


Iteration is carried out using recursion, as in Scheme.

Again make sure you understand how ML has determined the type. For example in nth, the 1 says int and [] or hd says list

Mutually recursive functions have to be defined together as shown in the last example.

val (x,y) = (1,2);
val x = 1 : int
val y = 2 : int
- val x::y = [1,2,3]; val x = 1 : int val y = [2,3] : int list
- val x::y::z = [1,2,3]; val x = 1 : int val y = 2 : int val z = [3] : int list


Patterns in ML are similar to Perl or Prolog, though much less powerful than either of those.

The idea is that it matches a constructor with variables against an object and uses the type of the object to figure out the types of the constituents of the constructor.

On the right we see examples of matching tuples and lists.

Note that for both list examples only one matching is possible.

Patterns in Functions

You can give multiple possible parameters for a function with a separate body for each. When the function is invoked, ML pattern matches the argument against the possible parameters and executes the appropriate body. I find this very cute.

- fun fib 1=1 | fib 2=1 | fib N = fib(N-1) + fib(N-2);
val fib = fn : int -> int
- fib 10;
val it = 55 : int
- fun doubleList [] = [] | doubleList L = 2*hd(L)::doubleList(tl(L)); val doubleList = fn : int list -> int list - doubleList [2,3,5]; val it = [4,6,10] : int list
- fun append ([] , ys) = ys = | append (x::xs, ys) = x::append (xs, ys); val append = fn : 'a list * 'a list -> 'a list
- fun last [h] = h | last (h::t) = last t; stdIn:18.3-18.38 Warning: match nonexhaustive h :: nil => ... h :: t => ... val last = fn : 'a list -> 'a - last [2,3,5]; val it = 5 : int - fun last [h] = h | last (h::t) = last t | last [] = 0; val last = fn : int list -> int

The first example is fairly simple: we do different things for different integer values of N.

The second example separates out the empty list. Note that in both cases the result is a (possibly empty) list of integers. ML has inferred that L is an integer since we multiply it by 2.

Write doubleList in Scheme on the board.

The append function illustrates how you divide lists into empty and those that are the cons of an element and a (smaller) list.

The function last is perhaps more interesting. It is polymorphic as it can take a list of anything (note that ML declares its argument to be of type 'a list and it return value to be of type 'a.

Note the warning that the match was nonexhaustive (i.e., not all cases were covered). The missing case is when h=[]. last([]) cannot be [] since that is of type 'a list not 'a. If you arbitrarily say last [] = 0, you lose polymorphism!

- fun twoToN(N) =
  if N=0 then 1
  else let val p = twoToN(N-1)
       in p+p
twoToN = fn : int -> int
- twoToN 5;
val it = 32 : int
let val J=5 val I=J+1 val L=[I,J] val Z=(J,L) in (J,I,L,Z) end; val it = (5,6,[6,5],(5,[6,5])) : int * int * int list * (int * int list)

Local Variables

As mentioned above ML is lexically scoped. For this to be useful, we need a way to introduce a lexical scope. Like Scheme ML uses let for this purpose.

The syntax is different, of course. The object-name-and-value pairs are written as definitions and appear between let and in. Following in comes the body and finally end.

The ML let is actually much closer to let* in Scheme in that the bindings are done sequentially. The second example illustrates the sequential binding. If the first two val's were done in the reverse order, an error results since J would not have been defined before it was used.

fun reverse(L) =
let fun reverse1(L,M) =
          if L=[] then M
          else reverse1(tl(L),hd(L)::M);
    in reverse1(L,[])
val reverse = fn : ''a list -> ''a list
- reverse [3,4,5]; val it = [5,4,3] : int list

Lexical Embedding of Functions

As you would expect, in addition to nested blocks (let above), ML supports nested functions. Once again let is used to indicate the nested scope.

One use of nested functions is to permit the definition of helper functions as in the reverse code on the right.

Note that this implementation of reverse is tail recursive.

Do you see why tail recursion is aided by applicative-order reduction?

fun applyList(f,[]) = [] |
    applyList(f,h::t) = f(h)::applyList(f,t);
val applyList = fn : ('a -> 'b) * 'a list -> 'b list
fun add3(n) = n+3; val add3 = fn : int -> int
- applyList(add3,[2,3,5]); val it = [5,6,8] : int list
fun sum(f,l,u) = if u<=l then 0 else f(u) + sum(f,l,u-1); val sum = fn : (int -> int) * int * int -> int

(First Class) Functional Programming

As with Scheme, ML functions are first class values. They can be created at run-time, passed as arguments, and returned by functions.

Passing Functions as Arguments: Compare applyList on the right to the FILTER function you wrote in Scheme for lab1.

Note that applyList is not tail-recursive; its last act is not the recursive call, but instead a ::.

It needs a helper function applyList1(f,done,rest). It is different from reverse1 because now we are building the result from left to right not right to left. You can't cons an element onto the right of a list; instead you do concatenation.

- val f = fn x => x+1;
val f = fn : int -> int
- sum(fn x => x*x*x, 0,10); val it = 3025 : int
- (fn x => x*x*x) (6); val it = 216 : int

Anonymous Functions: Our use of fun is similar to using both (define and (lambda in Scheme. We could separate fun into its two parts as shown in the first example on the right, val, which is like (define, and fn...=>, which is like lambda.

Looking back at our previous uses of fun, we see that ML responds by using val and fn.

Sometimes we don't need a name for the function, i.e., it is anonymous. This usage is illustrated in the second and third examples, with the anonymous function x3.

- fun adder(N) = fn X => X+N;
val adder = fn : int -> int -> int
- adder 7 5 val it = 12 : int - adder (7,5); stdIn:1.1-3.2 Error: operator and operand don't agree [tycon mismatch] operator domain: int operand: int * int in expression: adder (7,5)
- val add3 = adder 3; val add3 = fn : int -> int - add3 7; val it = 10 : int

Returning Functions as Values On the right we see a simple example of one function adder returning another (anonymous) function as a result. Note the type ML assigns to adder. The → operator is right associative so the type says that adder takes an integer and returns a function from integers to integers.

Write adder on the board in Scheme and note the double lambda.

Having functions return functions gives an alternative method for defining multi-variable functions. This method, known as Currying, after Haskell Curry, is illustrated on the right. Make sure you understand why adder 7 5 works and why adder(7,5) gives a type error!

We can define functions like add3 above in terms of adder.

val add3 =
  let val I=3
  in fn X => X+I
val add3 = fn : int -> int
add3 7;
val it = 10 : int;

Closures: As in Scheme it is possible to form a closure; that is, to include the local environment. In the simple case on the right, the environment is just the value of the identifier I. With the limited subset of ML that we are studying, closures do not play an important role.

If you are interested, see the end of lecture 5 where I just added a short section on closures in Scheme. Naturally it is optional for this semester.

Type Inference

As mentioned several time, type inference is an important feature of ML, one that distinguishes it from all the languages we have see so far.

Recall the following characteristics of the ML type system.

Why Does ML Do It? That it, what are the advantages of type inference.

How Does ML Do It? It is not magical, but is not trivial either! I believe the most common name for the procedure adopted by ML and other type-inferring languages is the Hindley-Milner Algorithm, but it is also sometimes referred to as Algorithm W or the Damas-Milner Algorithm.

We won't try to prove that it always works, but do note some of the facts (i.e., constraints) the algorithm uses.

The algorithm then proceeds very roughly as follows.

For a detailed discussion of type inference in ML, see Ben Goldberg's class notes (for Honors PL), Polymorphic Type Inference

Example 1: Draw my diagram on the board.

fun sum(f,l,u) =
  if u<=l then 0
   else f(u) + sum(f,l,u-1);
val sum = fn : (int → int) * int * int → int

Assign type(u)='a, type(l)='b, type(f)='c→'d, type(sum)='g →'h.
(Syntactically, f has to be a function.)

Since sum can return 0, of type int, 'h=int.

Using the expression u-1, since - is either of type int*int→int or real*real→real, and type(1)=int, infer 'a=int.

Using the expression f(u), infer 'c=int.

Using the expression u<l, since < is either of type int*int→bool or real*real→bool, and type(u)=int, infer 'b=int.

Using the expression f(u) + sum(f,l,u-1), since + is either of type int*int→int or real*real→real, and 'h=type(sum(...))=int,'d=int.

Note that 'g=type(f,l,u)=(int→int)*int*int.

Example 2: There is an alternate formulation below.

fun applyList(f,l) =
  if (l=[]) then []
   else f(hd(l))::applyList(f,tl(l));

Assign type(l)='a, type(f)='b→'c, type(applyList)='d→'e, type([])='g list, type(hd)='h list→'h, and type(tl)='i list→i.

From the expression l=[], since [] is of type 'g list, deduce that 'a='g list.

Since hd is of type 'h list→'h, and l is of type 'g list, infer the expression hd(l) has type 'g. Hence infer that 'b='g.

Since tl is of type 'i list→'i list and l is of type 'g list, infer that tl(l) is of type 'g list.

Since :: is of type 'j*'j list→'j list, and f(hd(L)) is of type 'c, infer that f(hd(l))::tl(l)) is of type 'c list.

Since applyList may return f(hd(l))::tl(l), infer that 'e='c list.

Thus applyList is of type (fun 'g→'c) * 'g list→'c list.

An Alternate Formulation of Example2

1st[]'g list
hd'h list→'h
tl'i list→'i
::'j * 'j list → 'j list
2nd[]'k list
Code SegImplication
l=[]=>'a = 'g list
hd(l)=>'h list = 'a = 'g list => 'h = 'g
f(hd(l))=>'b = 'h = 'g
tl(l)=>i' list = 'a = 'a list =>; 'i = 'g
f(...)::=>'j = 'i
can return ::=>'e = 'j list = 'i list = 'c list
arg=(f,l)=>'d = ('b→'c) * 'a list = ('g→'c) *g list
Hence applyList='d→'e = (('g→'c) * 'g list)→'c list
    fun applyList(f,l) =
      if (l=[]) then []
       else f(hd(l))::

fun largeType(w,x,y,z) =
  x=(w,w) andalso y=(x,x) andalso z=(y,y);

val largeType = fn : ''a * (''a * ''a) * ((''a * ''a) * (''a * ''a)) * (((''a * ''a) * (''a * ''a)) * ((''a * ''a) * (''a * ''a))) -> bool

Example 3: This weird example shows that it is possible for the size of a type to be exponential in the size of the function.

The operator andalso is the normal Boolean and. Recall that and has another meaning in ML. Similarly orelse is the normal Boolean or

Homework: CYU 54. Explain how the type inference of ML leads naturally to polymorphism.

Homework: CYU 57 How do lists in ML differ from those of Lisp and Scheme?

Homework: Write an ML program that accepts a list of strings and responds by printing the first string in the list. If the list is empty it prints "no strings".

Homework: Write an ML program that accepts a list of integers and responds by printing the first integer in the list. If the list is empty it prints "no integers".

Start Lecture #9


This topic includes the material in 3.3.4, 3.3.5, and 3.7 as well as additional material not in the text.

Chapter 3: Names, Scopes, and Bindings (continued)

3.3: Scope Rules (continued)

3.3.4: Modules

The Challenge of Monster Codes

A very serious problem in real-life computing is the super-linear rise in complexity of computer software. That is, if monolithic program 2 is twice as large as monolithic program 1, it is more that twice as complicated. As a very rough approximation, if program 1 has N things that can affect each other that gives N2 possible interactions. For program 2, the numbers are 2N and 4N2.

Thus, the following techniques prove to be very useful.

  1. Break a size 2N program into 2 size N pieces that interact very weakly.
  2. Reduce the complexity of each piece.
In this section, we mostly study technique 1. Languages without (or with limited) side effects are believed to support technique 2, as is good programming style in any language.

There have been many cute quotes concerning the deleterious effects of program complexity; my favorite is from Tony Hoare (Sir Charles Antony Richard Hoare, quicksort, Hoare logic, CSP).

There are two ways of constructing a software design: one way is to make it so simple that there are obviously no deļ¬ciencies, and the other is to make it so complicated that there are no obvious deficiencies.

There are additional benefits of information hiding, which include

Encapsulating Data and Subroutines

We have already seen an important method to split a program into pieces and to hide (some of) the details of one piece from the other pieces: namely the subroutine.

Certainly if you are given subroutine sort(A,B,n) that sorts the array A of size n, putting the result into array B, you do not need to worry if the algorithm used is bubble sort, heap sort, (Hoare's) quicksort, etc. Moreover, any local variables in sort are safe from your misuse.

The difficulty with subroutines is that they only solve a part of the problem. For example, how about a queue package that has two subroutines as well as some private data?

For this reason modules have been introduced in many new languages and retrofitted in some old ones.

Modules as Abstractions

Definition: A module is a programming language construct that enables problem decomposition and information hiding. Specifically a module

Modules were introduced by Barbara Liskov (recent Turing award winner) in her language Clu; she called them clusters (hence the name of the language). As an aside a Liskov design was one of four finalists in the competition for the design of Ada. Another early language using modules was Modula (Wirth), which begot Modula 2 and Modula 3. Ada 83 was one of the next languages to support modules. Now many languages do.

Although the concept of modules is now widely accepted, the name module is not. As mentioned they were called clusters in Clu. All versions of Ada call them packages as do Java and Perl. C++ C#, and PHP call them namespaces. C permits a weak form of modules through the separate compilation facility.

A module is somewhat like a record, but with a crucial distinction.

Imports and Exports

As mentioned above, an important feature of modules is the selective exporting of name: Only those names in the public interface can be accessed outside the module; those in the private implementation cannot.

Finer control is also possible. For example a name may be exported read-only or its internals may not be exposed.

A question arises as to whether the public interface is automatically imported into all other components. For example, if a queue module exports insert as part of its public interface, is the name insert automatically part of the environment of every other module or must these other modules specifically import the name? Modules for which the importation is automatic are said to have open scopes; those for which a specific import is needed are said to have closed scopes.

Ada, Java, C#, and Python take a middle ground. If module stack exports the name push, then all other modules have a compound name something like stack.push in their environment. If, in addition module B specifically imports names from stack, then the simple name push is available. In Ada, it is actually a little more complicated: if module A must issue a with of module B in order to access the items in B with dotted notation. If, in addition A issues a use of B, then A can use the short form.

Modules as Managers

One can imagine an application that needed exactly one queue which is encapsulated together with insert and remove in a module you might call TheQueue. More common, however, is to have a module called Queues, which defines a type Queue. Then a user of that module writes Q1:Queue and Q2:Queue to obtain two queues.

In this common case one says that the module manages the type Queue. Note that insert and remove now need to be given the specific Queue as an argument. So you might see something like the left hand procedure call

    Queues.Insert(element,Q1);           Q1.Insert(element);
In the next section, we will see that an object oriented viewpoint might lead to the right hand invocation.

Language Choices

Different languages use different terminology for modules and support somewhat different functionality. For example:

package QPack is
   procedure Insert (X:Float);
   function Remove Return Float;
end QPack;

package body QPack is Size : constant Natural := 50; type QIndex is new Integer range 0..Size; type Queue is array (QIndex) of Float; TheQueue : Queue; Front, Rear : QIndex :=0; procedure Insert (X:Float) is begin Rear:=(Rear+1) mod Qindex(Size); TheQueue(Rear):=X; end Insert; function Remove return Float is begin Front:=(Front+1) mod Qindex'Last; return TheQueue(Front); end Remove; end QPack;
with QPack; with Text_IO; procedure Main1 is X: Float; begin QPack.Insert(15.0); QPack.Insert(9.5); X:=QPack.Remove; if X=15.0 then Text_IO.Put("OK"); else Text_IO.Put("NG"); end if; end Main1;

A Simple One-Queue Ada Package

On the right is a full implementation and use of a very simple module, namely an Ada package for a single (size 50) queue (of floats). Note the three parts: the package specification or public interface, the package body, or private implementation, and the use of the package in a client procedure. The example on the right exports two procedures.

  1. The package specification includes only declarations (not definitions, those come in the body) for the subroutines Insert and Remove. The queue itself is not visible outside the package.

  2. The package body gives the implementation of the two routines as well as the necessary declarations. In particular TheQueue is define here. My code is a poor implementation of a queue: At the very least it should check for full and empty queues and raise an exception if they occur. Moreover, most queue implementations allows the user to specify the size of the queue rather than hard-wiring the size as I have done.

    Note the conversion Qindex(Size). Since Size is a Natural and Rear+1 is a Qindex, one must be converted to the other so that the mod can be applied. For the second mod, I used a different method: Given any discrete type, the attribute 'Last gives the last element.

    Note that TheQueue is indexed only by the variables Rear and Front, which are of type Qindex. Hence a successful compilation guarantees that I am not accessing TheQueue outside its definition. Ada is indeed statically typed! To be fair a run-time check might be needed for statements like

            Rear  := (Rear+1)  mod Qindex(Size);
            Front := (Front+1) mod Qindex'Last;
    to ensure that the range constraints are met for the assignment. I don't know if the gnat Ada compiler is smart enough to realize the 2nd version can't be out of bounds, but it is computable at compile time. The 1st version can't be out of bounds either, but that takes an additional step of logic to determine.

  3. The procedure Main1 uses Insert and Remove in a trivial manner. There are two points to note. First, the with statements are required to access the external modules, Q, which I wrote, and Text_IO, which is part of the standard Ada library. Even having with I needed to use names like Q.Insert. Second, by adding use statements, the dotted notation can be avoided.

package QQpack is
   type Queue is private;
   procedure Insert (X : Float; Q : in out Queue);
   procedure Remove (X : out Float; Q : in out Queue);
   QueueFull, QueueEmpty : exception;
   Size : constant Natural := 50;
   type QIndex is new Integer range 0..Size;
   type Contents is array (Qindex) of Float;
   type Queue is record
      Front : Qindex := 0;
      Rear  : Qindex := 0;
      NItems: Natural range 0..Size := 0;
      Item  : Contents;
   end record;
end QQPack;

package body QQPack is procedure Insert (X : Float; Q : in out Queue) is begin if Q.NItems=Size then raise QueueFull; end if; Q.NItems := Q.NItems+1; Q.Rear := (Q.Rear+1) mod Qindex(Size); Q.Item(Q.Rear) := X; end Insert; procedure Remove (X : out Float; Q : in out Queue) is begin if Q.NItems=0 then raise QueueEmpty; end if; Q.NItems := Q.NItems-1; Q.Front := (Q.Front+1) mod Qindex'Last; X := Q.Item(Q.Front); end Remove; end QQPack;
with QQPack; use QQPack; with Text_IO; use Text_IO; procedure Main2 is X: Float; TheQueue : Queue; begin Insert(15.0, TheQueue); Insert(9.5, TheQueue); Remove(X,TheQueue); if X=15.0 then Put_Line("OK"); else Put_Line("NG"); end if; exception when QueueFull => Put_Line ("Queue Full"); when QueueEmpty => Put_Line ("Queue Empty"); end Main2;

A Better Ada Queue Package

On the right, we now see a better queue package. A major change between the previous package and this one is that this package is for many queues not just one. Instead of implementing a queue, we now make public the type of the queue and have the user allocate however many queues they want.

We again see three parts to the implementation: The package specification, the package body, and the procedure using the package. However, this time the package specification has two parts, the normal public section and a new section labeled private.

  1. The package specification again includes declarations (not definitions) of the Insert and Remove procedures. There are two differences between the versions here and in the previous section. First, since the user may be declaring multiple queues, the procedures now have the specific queue as an additional (in out) argument. Second, Ada functions can have only in parameters so Remove is now a procedure with the removed value X as an out parameter.

    We see that the type Queue is declared here in the public section, but is declared to be private. What does this mean? It means that the user can declare objects of type Queue but cannot see inside those objects, i.e., cannot see the implementation. This is sometimes called an opaque declaration. In the private part of the specification, we see the details of the queue definition, including definitions of the types used within queue. Note that Front and Rear are now per-queue quantities. We added a count of the items present to detect full and empty queues.

    Finally, back in the public part, we see declarations of two exceptions whose lack we criticized in the previous version.

  2. The package body contains only the definitions of Insert and Remove. It includes the natural code to count items and detect full and empty queues. It raises the QueueFull and QueueEmpty exceptions when appropriate.

  3. The procedure Main2 declares a queue and uses it as expected. It could just as easily declare more queues. Note the exception section at the end of the procedure. If any of the statements between begin and exception raise either the QueueFull or QueueEmpty exception, the corresponding when clause will be triggered and the appropriate message will be printed. The exceptions are available because they appeared in the specification. However, to determine what events can trigger them, we need to look at the package body.

    Note that we included use statements for QQ and Text_IO. Had we not, Insert, Remove, QueueFull and QueueEmpty would require a QQ. prefix and Put_Line would require Text_IO.

   function Empty (Q : Queue) return Boolean;
   function Full  (Q : Queue) return Boolean;
   function "=" (Q1, Q2 : Queue) return Boolean;

function Empty (Q : Queue) return Boolean is begin return Q.NItems=0; end Empty; function Full (Q : Queue) return Boolean is begin return Q.NItems=Size; end Full; function "=" (Q1, Q2 : Queue) return Boolean is begin if Q1.NItems /= Q2.NItems then return False; end if; for J in 1..Q1.NItems loop if Q1.Item((Q1.Front+QIndex(J-1)) mod QIndex(Size)) /= Q2.Item((Q2.Front+Qindex(J-1)) mod QIndex(Size)) then return False; end if; end loop; return True; end "=";
AnotherQ : Queue; if TheQueue = AnotherQ then Put_Line("eq"); else Put_Line("ne"); end if;
if QQPack."="(TheQueue,AnotherQ) then

Some Extra Goodies On the right, in the first frame, we see three possible additions to public part of the package specification (they could be placed right after Remove). The corresponding additions to the package body are below the line.

The first two functions Empty and Full are trivial but, and this is important, the client can not write these functions since they use the private object NItems. With these two functions added, users can either test for full and empty themselves or can ignore the problem and process any QueueFull and QueueEmpty exceptions that occur.

The third function "=" is the equality comparison function. It simply checks that the two given queues have the same number of items and that corresponding items are equal. In Ada, when you define an equality predicate, as we did, the system automatically defines the corresponding inequality predicate.

If the user of QQPack includes a use QQPack statement, then the equality and inequality operators on Queues can be written simply as = and /=. For example, the third frame to the right contains inserts to the client code to utilize the queue equality predicate. First we define AnotherQ and then compare it to TheQueue. If no use statement is given, the if statement must be written as shown in the last frame.

Why Are They All Equal Size Queues of Floats? Because we haven't learned about Ada generics ... yet.

namespace stack {
    void push (char);
    char pop();

#include <stdio.h> #include "stack.h" namespace stack { const unsigned int MaxSize = 50; char item[MaxSize]; unsigned int NItems = 0; void push (char c) { if (NItems >= MaxSize) printf("Stack overflow!\n"); item[NItems++] = c; } char pop () { if (NItems <=0) printf("Stack underflow!\n"); return item[--NItems]; } }
#include "stack.h" int main(int argc, char *argv[]) { stack::push('c'); if (stack::pop() != 'c') return 1; return 0; }
using namespace stack;
using stack::push;

A Simple One-Stack C++ Namespaces

Namespaces are a late addition to C++ that serve some of the same functions as Ada packages. They encapsulate related entities and privatize names. This privatization permits a developers of a namespace to define "namespace global" names such as error_count without fear of clashing with other namespaces.

On the right we see three programs stack.h, stack.cpp, and main1.cpp, that correspond to the familiar specification, implementation, client usage pattern we used above with Ada. Indeed, the pieces do correspond roughly to their Ada counterparts.

One difference between modules in the two language is that C++ is more filename oriented. Whereas in ada the client asked for the name of the package; in C++, the client includes the file by filename. Although no one would do it, I tried replacing all occurrences of stack.h with x.y and everything worked.

Another effect of the filename versus package name difference is that the C++ module implementation (stack.cpp) #includes the module specification (stack.h); whereas, in Ada this is automatic (e.g., in the better queue implementation, Size was defined in the specification and used without declaration in the body). In the very simple C++ code on the right there is no real need for the insertion since everything in .h file is in the .cpp as well. However, the #inclusion does ensure that the compiler check for consistent declarations (actually, g++ does not check very well; I changed the .h file so the push took a an integer argument and the .cpp file still compiled. But keep this quiet.

using Declarations and Directives The C++ using directive is similar to the Ada use statement. For example the statement in the fourth frame on the right just after the #include in the client code would permit programmer to elide both uses of :: in main. Unlike Ada, C++ has another form of the using directive that imports just a single name. The bottom frame shows a statement that would permit eliding the :: prefixing push, but not the :: prefixing pop.

What about C++ Generics? They are called templates and will be discussed later.

Argument Dependent Lookup (ADL) in C++

C++ has a feature in which an unqualified function name is searched automatically in several namespaces, depending on the types of the arguments at the call site. This rather technical feature is also called argument dependent name lookup and Koenig lookup, named after Andrew Koenig. We will not be using it.

Non-Holistic Aspects of the C++ Module System

The C++ compiler does not check to see that all entities declared in the visible interface (.h file) are actually defined in the implementation (.cpp files) or that multiple different definitions are given by the various .cpp files. This is because compilation is done just one file at a time. Consequently errors of this kind are found by the linker. In contrast, the package system is fully understood and checked by the Ada compiler.

Abbreviating Module Names in Ada and C++

Both Ada and C++ have simple facilities for abbreviating long module names. This becomes especially important when you have submodules. So if entity x is in module B which is a submodule of module A we would write A.B.x in Ada and A::B::x in C++.

package P1 renames A.Very_Long.Nested.Package_Name;
namespace n1 = a.very_long.nested.namespace_name;

The abbreviation facility is called renames in Ada and is written simply as = in C++. Both are illustrated on the right.

Packages in Java

Modules in Java are called packages and again aid in privatizing names thereby preventing unintended name clashes. Packages are closely linked to directories in the file system. Normally one package is stored in one directory and the name of the package is the name of the directory. There are other possibilities and what follows is a simplification.

Recall that a .java file can contain at most one public class. For simplicity, we ignore non-public classes and all subclasses. Hence each file contains exactly one class, which is public. In this case the file name must be the class name with the suffix .java appended.

package package-name;

Ignoring whitespace and comments, the beginning of the file determines the package in to which the contained public class will be placed. If the file begins with a package statement as shown on the right, the class becomes part of package package-name. If the file does not begin with a package statement, the class becomes part of the default anonymous package.

Although the language definition actually permits more flexibility, it is recommended and we will assume that a package equals a directory, that is, we assume that all the .java files in directory .../pack1 are members of the pack1 package and no file outside .../pack1 is a member of pack1.

All the classes in a given package (i.e., all the .java files in a given directory) are directly visible to each other; no directive is required.

import package-name.class-name
import package-name.*

By default, different packages are completely independent, neither can access any of the entities in the other. This behavior can be modified by either of the import directives to the right. All import directives must occur immediately after the package statement. The first directive states that the class in the current file has access to class class-name from package package-name. The second directive states that the class in the current file has access to all the classes in package package-name.

signature STACKS =
   type stack
   exception Underflow
   val empty : stack
   val push : char * stack -> stack
   val pop : stack -> char * stack
   val isEmpty : stack -> bool

structure Stacks : STACKS = struct type stack = char list exception Underflow val empty = [ ] val push = op:: fun pop (c::cs) = (c, cs) | pop [] = raise Underflow fun isEmpty [] = true | isEmpty _ = false end;
- val s1 = Stacks.empty; val s1 = [] : Stacks.stack
- val s2 = Stacks.push (#"x",s1); val s2 = [#"x"] : Stacks.stack
- val s3 = Stacks.push(#"y",s2); val s3 = [#"y",#"x"] : Stacks.stack
- val (x,s4) = Stacks.pop s3; val x = #"y" : char val s4 = [#"x"] : Stacks.stack
- Stacks.isEmpty (#2 (Stacks.pop s4)); val it = true : bool
- Stacks.isEmpty s4; val it = false : bool

Modules in ML

ML has a fairly rich module structure that we are not covering, but on the right is an illustration of the most basic aspects.

The first frame shows a signature, which is the module's visible interface. As we shall see, it is the constant empty (not to be confused with the predicate isEmpty) that serves as the constructor for the STACKS signature.

The next frame shows one structure for the STACKS signature. This structure is named Stacks.

Whereas Ada has exactly one implementation for each specification, ML is more liberal. The user can have several structures active at one time, each implementing the same signature (in different manners). As we shall see, the long form name of an entity includes the structure name, not the signature name. In this case the structure Stacks gives a list-based implementation of the signature STACKS. ML also permits a single structure to implement multiple signatures, but we will not discuss that feature.

The remaining frames show an ML session after the first two frames have been entered. We first create an empty stack, and push two chars on to it. Note that the following pop produces a 2-tuple of output, the value popped and the stack after popping.

We then pop s4, which has only one element and test the 2nd component of the output (the new stack) to verify that it is empty. Finally we confirm that s4 has indeed not been changed by popping; it is still not empty.

What if I Want Different Types of ML Stacks Read about functors, which are parameterized structures and correspond to Ada generics.

var A, B : queue  { queue is a module name }
var x, y : element

Module Types and Classes (and Object Orientation)

In Ada, C++, and Java a module can be viewed as an encapsulation or manager of data structures and operations on them. Consider our Ada treatment. We first defined a package that supported one queue. We could not instantiate this package several times to get several queues; instead we wrote a more elaborate package that permitted the user to declare multiple queues.

An alternative approach to modules has been adopted by Euclid (and to some extent ML, but we haven't exploited that). In Euclid a module is a type and hence a queue module would be a queue type (not just supply a queue type). The user then instantiates the module multiple times to get multiple queues and (logically) multiple insert and remove operators.

Note that, the sample Euclid code on the right instantiates two queues, each of which has an associated insert procedure. In Ada this would have been written Insert(X,A).

Object Orientation Viewing a module as a type (rather than as a manager) should remind you of object oriented programming. Indeed, a module type is quite similar to a class in an object oriented language, with the big exception that module types do not support inheritance. Note that, with both module types and classes, each instantiated object has defined an instance of all the operations associated with the module type or class.

Modules Containing Classes Since module types are so similar to inheritance-free classes, one might ask why an object oriented language like Java, which has a class structure, would include modules as well.

The answer is that they serve somewhat different purposes. Object orientation works well for multi-instance abstractions. Early on in your Java career, you learn about classes called rectangle, circle, triangle, etc each of which inherits from class shape. Then you instantiate many rectangles, circles, and triangles.

A prototypical module usage would be for a large problem that has a large functional subdivision. For example an aircraft control system has large subsystems concerned with guidance, aerodynamics, and life support. While you might have multiple aerodynamics codes using different algorithms for reliability, this is not the same as having multiple rectangles on a graphics canvas.

Homework: CYU 21, 22, 24, 26.

Homework: I provided two queue packages in Ada, consider the second (better) one. Produce a similar package for dequeues (double ended queues). These support 4 operations inserting and removing from either end. Name them InsertL, InsertR, RemoveL, RemoveR. Your solution should have three parts: dequeue.ads, the specification (interface), dequeue.adb, the body (implementation), and client.adb the client (user) code. Include the goodies. If you want to compile and run your code, ada is available on the nyu machines.

    gnatmake client.adb
will compile everything.

Start Lecture #10

Chapter 9. Data Abstraction and Object Orientation

We have jumped from the middle of chapter 3 to the beginning of chapter 9, which suggests that there will be a great change of focus. However, this is not true and the beginning of chapter 9 flows perfectly after the part of chapter 3 we just finished.

As we mentioned at the end of last lecture, modules have much in common with the classes of object-oriented programming. Indeed, in one (admittedly less common) formulation of modules, namely modules as types, modules are quite close to classes without inheritance.

However, do not think that classes are a new and improved version of modules. The primary ideas of object oriented programming actually predate modules.

9.1: Object-Oriented Programming

With modules we have already gained these three benefits.

  1. The conceptual load has been reduced by information hiding.
  2. Fault containment is increased by denying client access to the implementation.
  3. Program components are more loosely coupled since only changes to the module interface can require changes to client code.

We are hoping for more. In particular we want to increase the reuse of code when the requirements change very little. Object-oriented programming can be seen as an attempt to enhance opportunities for code reuse by making it easy to define new abstractions as extensions or refinements of existing abstractions.

What is Object-Oriented Programming (OOP)

Generally OOP is considered to encompass three concepts.

  1. An abstract data type (ADT).
  2. Inheritance.
  3. Dynamic binding.

Lets describe these in turn.

Abstract Data Type Recall the module-as-type formulation, in which we instantiate the module multiple times to obtain multiple copies of the data and operations. When described in OOP terminology we say that the idea of an object is to

An object is an entity containing

A class is a construct defining the data and methods contained in all its instances (which are objects).

Inheritance With inheritance, classes can be extended

If class B extents class A, B is called a subclass (or a derived or child class) of A, and A is called a superclass (or a base or parent class) of B.

Dynamic Binding Whenever an instance (i.e., object) of a class A is required, we can use instead an instance of any class derived (either directly or indirectly) from A.

Examples from the CD

#include <iostream>
using std::cout;
using std::flush;

class list_err { public: const char *description; list_err(const char *s) {description = s;} };
class list_node { list_node* prev; list_node* next; list_node* head_node; public: int val; list_node() { prev = next = head_node = this; val = 0; } list_node* predecessor() { if (prev == this || prev == head_node) return 0; return prev; } list_node* successor() { if (next == this || next == head_node) return 0; return next; } int singleton() { return (prev == this); } void insert_before(list_node* new_node) { if (!new_node->singleton()) throw new list_err ("attempt to insert listed node"); prev->next = new_node; new_node->prev = prev; new_node->next = this; prev = new_node; new_node->head_node = head_node; } void remove() { if (singleton()) throw new list_err ("attempt to remove unlisted node"); prev->next = next; next->prev = prev; prev = next = head_node = this; } ~list_node() { if (!singleton()) throw new list_err ("attempt to delete listed node"); } };
class list { list_node header; public: int empty() { return header.singleton(); } list_node* head() { return header.successor(); } void append(list_node *new_node) { header.insert_before(new_node); } ~list() { if (!header.singleton()) throw new list_err ("attempt to delete non-empty list"); } };
class queue : public list { public: void enqueue(list_node* new_node) { append(new_node); } list_node* dequeue() { if (empty()) throw new list_err ("attempt to dequeue empty queue"); list_node* p = head(); p->remove(); return p; } };
void test() { list my_list; list_node *p; for (int i = 0; i < 4; i++) { p = new list_node(); p->val = i; my_list.append(p); } p = my_list.head(); for (int i = 0; i < 4; i++) { cout << p << ' ' << p->val << ' ' << p->successor() << '\n'; p = p->successor(); } p = my_list.head(); while (p) { cout << p->val << '\n'; list_node *q = p->successor(); p->remove(); delete p; p = q; } queue Q; for (int i = 0; i < 4; i++) { p = new list_node(); p->val = i; Q.enqueue(p); } cout << "queue:\n"; while (1) { p = Q.dequeue(); cout << p->val << '\n' << flush; delete p; } } int main() { try { test(); } catch(list_err* e) { cout << e->description << '\n'; } }
0x603010 0 0x603040 0x603040 1 0x603070 0x603070 2 0x6030a0 0x6030a0 3 0 0 1 2 3 queue: 0 1 2 3 attempt to dequeue from empty queue

The code on the right is from the CD. It gives a C++ implementation of a (circular) list of integers. The entire file can be compiled at once with g++.

The basic idea is that a list is composed of list nodes, one of which is the list header. The list is doubly linked and circular with each node having an additional pointer (called head_node) to the list header. When a node is created, its prev, next, and head_node pointers are set to point to itself. A node in this state is called a singleton. Creating a list just consists of creating one node, which becomes the header of the list.

The code in the first frame just includes a standard library and enables referencing two I/O members without using the full :: notation.

The next frame captures error messages. It is actually a little fancier (see below).

Public and Private Members

The third frame is the list_node class. Note that modules, like classes restrict the names accessible by clients. However, unlike modules, classes must take inheritance into account. In C++ there are three levels of accessibility.

Private is the default so the prev, next, and head_node pointer declarations are available only in this class.

In addition a class C1 can specify that specific external subroutines and external classes are friends. Such friends have access to all of C1's data and methods

In this particular class, all the rest is public. The val field holds the (integer) value contained in this node. The list_node() method is important and special. Since it has the same name as the class, it is the constructor for the class and is called automatically whenever an object is instantiated. In this case it sets the three (private) pointer fields to refer to the node itself thereby creating a singleton. Note that this is a reserved word and always refers to the current object.

When first created, a node is a singleton. However, we shall soon see that nodes can be inserted before other nodes thereby making non-trivial lists

The predecessor and successor methods return the appropriate node of such a list. In case the corresponding node doesn't exist (e.g., the node right before the head—remember these will be circular lists—has no successor), the method returns 0. Does the 0 signify the integer before 1, the null list_node* pointer, or the Boolean false? Next is the singleton predicate, which is straightforward.

The next two methods, which insert and remove an element seem, at first glance, to be misplaced. Shouldn't they be in the list class? Looking more closely, we see that the insert procedure really isn't inserting the new node onto a certain list, it is inserting the node before another node (and hence onto the same list as the old node). Similarly the remove method just plays with the predecessor and successor nodes and thereby removes the current node from whatever list it was on. There is error checking to be sure you don't insert a node onto a second list or remove a node not on any list.

Finally, we see the destructor, i.e., the method whose name is the class name prefixed with a ~. This method is automatically invoked when the node is delete'd (similar to free in C) or goes out of scope. The system would reclaim the storage in any case, the destructor is used for error checking and for managing other storage. In this case we check to be sure the node is not on a list (since reclaiming storage for such a node would leave dangling references in the predecessor and successor).

After plowing through the rather fancy node class, the list itself, shown in the next frame, is rather simple. The only data member is the header, which is a list_node and hence automatically initialized to be a singleton. The first method head returns a pointer to the first element on the list (this successor of the header). For an empty list this is a pointer to a the header. The second method append adds the new node to the end of the list by making it the predecessor of the header. The destructor makes sure the list is empty, by ensuring that the header is a singleton.

Tiny Subroutines

It is quite noticeable how small each method is. Although other, more complicated, examples will have larger methods, it is nonetheless true that OOP does encourage small methods since methods are often used in situations where a more monolithic programming style would use direct access to the data items. (This has negative effects from a computer architecture viewpoint, but we will not discuss that point.) For example, it would be more in the OOP style to make the val field private and have trivial public methods get_val and set_val that access and update val. Indeed, C# has extra syntax to make this especially convenient.

Derived Classes

The next frame shows how to derive one class from another. Specifically, we derive a queue class from our list class. This is indicated in the class statement by writing public list. The keyword public makes all the methods and data members of list accessible to clients of queue. Since the append function in the node class adds the new node to the end (tail) of the list it works fine as enqueue. We need dequeue to remove the first entry and, guess what, our list class has a head() method to give us the head entry, which our node class can then remove.

In this example we just added two methods to the base class. It is also possible to redefine existing methods and to decrease the visibility of the components of the base class.

Testing the Class

The next frame is the client code using these classes; it mostly plays around with them. Note that some objects are declared and hence have stack-like lifetime, whereas others are heap allocated. The last frame is the output generated by running the client.

By declaring my_list we obtain the header. Using this list (i.e., the header), we heap allocate some nodes and append them to the list giving them val's of 0, 1, 2, 3. We then print out the values together with the node's predecessors and successors. The final zero in the successor column is the same zero we commented on earlier (the successor of the last node is the header). Then we delete the nodes, again printing out the val's.

Much the same is done for queues. A difference is the fancy exception handling to catch the event of trying to dequeue an empty queue.

#include <iostream>
using std::cout;
using std::flush;
class list_err {
  const char *description;
  list_err(const char *s) {description = s;}
class gp_list_node {
  gp_list_node* prev;
  gp_list_node* next;
  gp_list_node* head_node;
  gp_list_node() : prev(this), next(this),
                   head_node(this) {
  gp_list_node* predecessor() {
    if (prev == this || prev == head_node)
      return 0;
    return prev;
  gp_list_node* successor() {
    if (next == this || next == head_node)
      return 0;
    return next;
  bool singleton() {
     return (prev == this);
  void insert_before(gp_list_node* new_node) {
    if (!new_node->singleton()) {
      throw new list_err
        ("attempt to insert listed node");
     prev->next = new_node;
     new_node->prev = prev;
     new_node->next = this;
     prev = new_node;
     new_node->head_node = head_node;
  void remove() {
    if (singleton()) throw new list_err
        ("attempt to remove unlisted node");
    prev->next = next;
    next->prev = prev;
    prev = next = head_node = this;
  ~gp_list_node() {
    if (!singleton()) throw new list_err
        ("attempt to delete listed node");
class list {
  gp_list_node head_node;
  int empty() {
    return head_node.singleton();
  gp_list_node* head() {
    return head_node.successor();
  void append(gp_list_node *new_node) {
  ~list() {
    if (!head_node.singleton())
      throw new list_err
        ("attempt to delete non-empty list");
class queue : private list {
  using list::empty;
  using list::head;
  void enqueue(gp_list_node* new_node) {
  gp_list_node* dequeue() {
    if (empty()) throw new list_err
        ("attempt to dequeue empty queue");
    gp_list_node* p = head();
    return p;

Another Example from the CD

This example generalizes the previous one. We study it not only for the improved generality, but also for the additional C++ features that are used

General-Purpose Base Classes

What if we wanted to have lists/queues of floats? of chars?

One could, as shown on the right start with a generalized_list_node class that includes only the list-like aspects of list-node (basically everything except val).

Note that the constructor for this class looks weird (at least to me, a common occurrence for C++ code). This notation is a fast way of initializing various fields and, for this example, has the same effect as

    gp_list_node() {
      prev = this;
      next = this;
      head_node = this;
Below we show an example where the two initialization methods differ.

Classes list and queue are derived from the generalized class and again do not mention val.

Then we derive classes such as int_list_node (shown in the next frame), float-list-node, etc. from generalized_list_node by adding an appropriately typed val.

Alternatively, and perhaps better, after learning about C++ generics (called templates), we would define a list_node<T> class and then instantiate it for whatever type T we need.

class int_list_node : public gp_list_node {
  int val;   // the actual data in a node
  int_list_node() {
    val = 0;
  int_list_node(int v) {
    val = v;
//  // complete rewrite:
//  void remove() {
//    if (!singleton()) {
//      prev->next = next;
//      next->prev = prev;
//      prev = next = head_node = this;
//    }
//  }
  // use existing but catch error:
  void remove() {
    try {
    } catch(list_err*) {
      ;   // do nothing
  int_list_node* predecessor() {
    return (int_list_node*)
  int_list_node* successor() {
    return (int_list_node*)
void test() {
  list L;
  int_list_node *p;
  for (int i = 0; i < 4; i++) {
    p = new int_list_node(i);
  p = (int_list_node*) (L.head());
  for (int i = 0; i < 4; i++) {
    cout << p << ' ' << p->val
         << ' ' << p->successor() << '\n';
    p = (int_list_node*) (p->successor());
  p = (int_list_node*) L.head();
  while (p) {
    cout << p->val << '\n';
    int_list_node *q = (int_list_node*)
    delete p;
    p = q;
  queue Q;
  for (int i = 0; i < 4; i++) {
    p = new int_list_node(i);
  cout << "queue:\n";
  while (1) {
    p = (int_list_node*) Q.dequeue();
    cout << p->val << ' ' << Q.empty()
                   << '\n' << flush;
    delete p;
int main() {
  try {
  } catch(list_err* e) {
    cout << e->description << '\n';

Overloaded Constructors

If we look at closely at int_list_node (shown to the right) we see that there are two constructors given: one with no parameters and one with a single int parameter. This feature of C++ permits the client code to construct an int_list_node with either zero or one argument. In this case the code treats the zero argument case as specifying a default value of zero for the one argument case. As you would expect, you can have other number of parameters and can have different constructors with the same number of parameters, but of different types. That is C++ chooses the constructor whose signature matches those in the object instantiation.

Modifying Base Class Methods

Up to now the derived class has inherited the base class in its entirety and simply added additional functionality. However the derived class can override base functionality as we now show.

Recall that list_node (from the previous example), when asked to remove a node that was not on a list (an unlisted node), would throw an error. Suppose this time we prefer to do nothing, that is removing an unlisted node should be a no-op. The next frame shows two implementations. The first solution, which is commented out in the code, is to simply do the removal only for the common case (a listed node). This solution can be criticized for relying on implementation details of list_node.

The second solution is to let list_node signal an error when removing an unlisted node but have int_list_node catch the error and do nothing.

Private Inheritance

In the first example queue inherited all of list (note the keyword public in the class statement. In the second example the inheritance is private and hence the public interface of list is not available to clients of queue. However, queue then explicitly adds empty and head to its namespace so the only name removed is append.


These examples illustrate a common occurrence in OOP, the need for one class to be a container (or collection) of another. In the examples we have seen queues and lists are containers of nodes.

Another implementation strategy, which would support heterogeneous lists, would be for lists to have their own nodes that contain a field that is a pointer (or reference) to an object. This approach is used in several standard libraries.

Homework: CYU: 1, 3, 4, 7, 10.

9.2: Encapsulation and Inheritance

Most of the chapter and essentially all of our coverage treats OOP as an extension of the module-as-type framework. This subsection recasts OOP from the module-as-manager framework. We will not cover this material in any detail at all.

9.2.1: Modules

The this Parameter

Making Do without Module Headers

9.2.2: Classes

9.2.3: Nesting (Inner Classes)

Classes can be nested in both Java and C++, which bring up a technicality when the several objects of the nested class are created and the outer class has non-static members.

9.2.4: Type Extensions

9.2.5: Extending without Inheritance

9.3: Initialization and Finalization

As we have seen C++, and most other OO (object-oriented) languages, provide mechanisms, constructors and destructors, to initialize and finalize objects. Formally:

Definition: A constructor is a special class method that is called automatically to initialize an object at the beginning of its lifetime.

Definition: A destructor is a special class method that is called automatically to finalize an object at the end of its lifetime.

We shall address the following four issues related to constructors and destructors.

  1. Choosing a constructor.
  2. References and values.
  3. Execution order.
  4. Garbage Collection.

9.3.1: Choosing a Constructor

Most OO languages permit a class to have multiple constructors. There are two principle methods used to select a specific constructor for an object.

9.3.2: References and Values

Recall that Java uses the reference model for objects. That means that a variable can contain a reference to an object but cannot contain an object as a value. (Java uses the value model for built in types so a variable can contain an int for a value.) Thus, every object must be created explicitly. (This is not true for int's: if x contains the integer 5, y=x; creates another integer without a create operation.) Since every object is created explicitly, a call to the constructor occurs automatically.

C++, like C, uses the value model so a variable can contain an object. (C also has references, but the point is that sometimes a variable contains an object not just a reference to it).

class Point {  // C++
  double x, y;
  Point() : x(0), y(0) {}
  Point(const Point& p) : x(p.x), y(p.y) {}
  Point (double xp, double yp) : x(xp), y(yp) {}
  void move (double dx, double dy) {
    x += dx;  y +=dy;
  virtual void display () { ... }

Point p; // Calls #1, the default constructor Point p2(1.,2.) // Calls #3 Point p3(p2); // Calls #2, the copy constructor Point p4 = p2; // Same as above

On the right we show simple C++ class with three different constructors. In the second frame are four object declarations.

  1. The first and simplest declaration includes no arguments. This matches the signature of the first constructor and hence p will have zero x and y coordinates. Again note the syntax in the constructor: the items between the : and the { are field initializers. The constructor with no parameters is called the default constructor
  2. The second declaration has two double arguments and hence matches the signature of the third constructor. This point, p2 will have coordinates (1.,2.).
  3. The third declaration occurs quite frequently. A new object is to be created based on an old object of the same class. This is called the copy constructor and we see that the constructor does indeed make p3 a copy of p2. (I believe a constructor with one parameter of type a reference to the class—i.e., classname&—is called the copy constructor even if the constructor does not make a copy.)
  4. Finally, the four declaration is just a syntactic variation of the third.

Point *q1, *q2, *q3; // no constructor calls
q1 = new Point(); // calls default constructor
q2 = new Point(1.,2.) // calls #3
q3 = new Point(*p1); // calls copy constructor

The above examples were all stack allocations. To the right we see examples with heap allocation. The first line shows three pointers being declared. Since the *qi are not Point's (they are pointers) there are no constructor invocations. The next three lines are the heap analog of the corresponding lines above and hence the constructors call are the same as above.

9.3.3: Execution Order

Next we must deal with inheritance. Assume we have a derived class ColoredPoint based on Point as shown below on the right. We need to execute the constructors from both the base and derived class and must do so in that order so that when the derived class constructor executes, it is dealing with a fully initialized object of the base class. The client code specifies the appropriate argument(s) to the derived class constructor but not the arguments to the base class (the client is dealing with ColoredPoint's and should not be thinking about constructing a Point).

enum Color {red, blue, green, yellow} ;
class ColoredPoint : public Point {
  Color color;
  ColoredPoint (Color c) : Point(), color(c) {
  ColoredPoint (double x, double y, Color c) :
    Point(x,y), color(c) {
  Color getColor () { return color; }
  void display () { ...}  // now in color

Consider the first constructor on the right. Although it has exactly one parameter, it is not a copy constructor since that parameter is not of type ColoredPoint&. Let's examine carefully the funny stuff between the colon and the braces.

The first item is Point(). Point is a class so we are invoking the default constructor for that class, which we recall sets the coordinates to zero. The compiler guarantees that the base-class constructor (Point in this case) is executed before the derived-class constructor (ColoredPoint) begins. Color is a field of the current class (ColoredPoint) so we are simply setting that field to c, the parameter of the constructor we are writing. Since this is all that is needed, there is no code between the braces.

The second constructor has three arguments. The first two are passed to the Point constructor where they become the coordinates of the constructed point. The third again is used to set the color. As before nothing further is needed between the braces.

We mentioned previously that the material between the colon and braces was in simple cases equivalent to placing the obvious analogues between the braces, where you would have expected them. We can now see some differences between putting items between the colon and the braces vs inside the braces. The ones between are done first. Moreover there is a subtle point that applies even without derived classes. To date all the fields of our classes have been simple types like float, or enun or a pointer. Assume instead that a field is another class say C1 and that obj1 is an object of class C1, then placing C1(obj1) between colon and braces results in calling the copy constructor of C1; whereas, placing it between the braces results in the default constructor being called and then an assignment of obj1 over-ridding the default value.

9.3.4: Garbage Collection

When a C++ object is destroyed the destructors for all the classes involved are called in the reverse order the constructors were called, i.e., from most derived all the way back to base.

The primary use of destructors in C++ is for manual storage reclamation. Thus OOP languages like Java and C# that include automatic garbage collection have little use for destructors. Indeed, the programmer cannot tell when garbage collection will occur so a destructor would be a poor idea. Instead, Java and C# offer finalize, which is called just prior to the object being garbage collected, whenever that happens to be. However, finalize is not widely used.

9.A: Java Differences

Here we show some differences between C++ and Java

The Meaning of Protected

In Java protected is extended to mean accessible within the class, derived classes, as well as within the packages in which the class and derived classes are declared.

No Private or Protected Derivation

Recall the line class queue : private list { from our early C++ code. Java permits only public extensions. Thus whereas a C++ derivation can decrease, but not increase, the visibility of the base class, a Java derivation can neither decrease nor increase the visibility of the base class.

  class Point {
    private double x,y;
    public Point () { this.x = 0;  this.y = 0; }
    public Point (double x, double y) { this.x = x;  this.y = y; }
    public void move (double dx, double dy) { x += dx;  y +=dy;  }
    public void display () { ... }

class ColoredPoint extends Point { private Color color; public ColoredPoint (double x, double y, Color c) { super (x,y); color = c; } public ColoredPoint (Color c) { super (0.0, 0.0); color = c; public Color getColor() { return color; } public void display () { ...} // now in color }
Point p1 = new Point(); Point p2 = new Point(1.5, 7.8); Point p3 = p2; // no constructor called

The Point and Colored Point Classes

On the right we see the java equivalent of the Point and ColoredPoint classes. The syntax is similar, but as we see there are differences. Most of these differences are small: the base class is called super, each item is tagged public or private, extends is used not a :, etc.

However, the last line reveals a real difference. In C++ that line would invoke the copy constructor and p3 would be a new point (independent of p2) that happens to be initialized to the current value of p2. In Java we do NOT have a new point. This is a consequence of Java having reference not value semantics for objects. The result is that p3 is just another reference to the point that p2 refers to. Changing the point (not just the reference) changes the point each of them refers to.

I believe this last difference is important enough to explain with another example of C++ and Java code for a Point class.

public class Point {
    public int x, y;
    public Point() {this.x=0; this.y=0;}
    public static void main (String[] args) {
    Point pt1 = new Point();
    Point pt2 = new Point();
    Point p = pt1;
    pt2 = pt1;
    System.out.println ("pt1.x is " + pt1.x);
    System.out.println ("pt2.x is " + pt2.x);
    System.out.println ("p.x is " + p.x);
    pt1.x = 1;
    System.out.println ("pt1.x is " + pt1.x);
    System.out.println ("pt2.x is " + pt2.x);
    System.out.println ("p.x is " + p.x);

pt1.x is 0 pt2.x is 0 p.x is 0 pt1.x is 1 pt2.x is 1 p.x is 1

Value vs Reference Semantics; Shallow vs Deep Copies

Java: The Java code on the right declares a very simple Point class. I has just 2 data fields, x and y both integers. The default constructor simply sets the two fields to zero.

The only method is main which begins execution. It instantiates two points pt1 and pt2 and declares another p. The declared point is then set equal to p1. As mentioned above, Java has reference semantics for points so all three variables are reference to points.

Two points have been created and are referred to as p1 and p2. Then p is declared and set to refer to the first point. Both points have zero x components. I then assigned pt1 to pt2. This has consequences! Due to reference semantics, pt2 and pt1 now refer to the same point. There are no remaining references to the point previously referred to by pt2 and hence this data can be garbage collected. Thus we have only one actual point with three references to it. Sure enough, changing the x component of pt1 changes the x component of all three references.

val-ref shallow-deep java
#include <stdio.h>
#include <stdlib.h>
class Point {
  int x, y, w[2], *z;
  Point() : x(0), y(0) {
    w[0] = w[1] = 3;
    z = new int[2];
    z[1] = 9;
int main(int argc, char *argv[]) {
  Point pt1, pt2;
  pt2 = pt1;
  printf("pt1: %d %d %d\n",pt1.x,pt1.w[1],pt1.z[1]);
  printf("pt2: %d %d %d\n",pt2.x,pt2.w[1],pt2.z[1]);
  pt1.x = 1;  pt1.w[1] = 5;  pt1.z[1] = 6;
  printf("pt1: %d %d %d\n",pt1.x,pt1.w[1],pt1.z[1]);
  printf("pt2: %d %d %d\n",pt2.x,pt2.w[1],pt2.z[1]);
  return 0;
pt1: 0 3 9 pt2: 0 3 9 pt1: 1 5 6 pt2: 0 3 6

C++: The C++ code illustrates both value semantics and shallow copies. To see the value semantics, just concentrate on the x field in the Point class. As in the Java code x is initialized to zero by the default constructor. The two points are simply declared, but with value semantics, this declaration creates points (via the default constructor). Assigning one to the other copies the point not the reference. Hence we still have two points each referenced by one variable and, therefore, changing the x component of one point does not affect the other.

Note the addition of w and z to the class Point. Each is in a sense an array of two integers (e.g., I print each using array notation). However z is heap allocated and the assignment of pt2 to pt1 results in only a shallow copy. That is only the pointer is copied not the corresponding integers. Thus changing pt1.z[1] changes the corresponding field of pt2 as well. Also, the heap space allocated in creating pt2.z is now unreferenced and hence inaccessible. Thus, we have leaked memory.

val-ref shallow-deep cpp

Homework: CYU 23, 26 (ignore Eiffel and Smalltalk), 30 (substitute Java for Eiffel).

Start Lecture #11

9.4: Dynamic Method Binding

Referring back to the Point/ColoredPoint pair of classes, we see that a ColoredPoint has all the properties of a Point (plus more). So any place where a Point would be allowed, we should be allowed to use a ColoredPoint.

Recalling Ada type terminology, the derived class acts like a subclass of the base class.

Definition: (Presumably) for this reason, the ability of a derived-class object to be used where a base-class object is expected, is called subtype polymorphism.

ColoredPoint *cp1 =
  new ColoredPoint (2.,3.,red);
Point *p1 = cp1;

Consider the code on the right. We create a ColoredPoint pointed to by cp1 and declare a Point pointer (sorry for the name) p1. We initialize the second pointer using the first one. This looks like a type mismatch, but is ok by subtype polymorphism. The question is, Which display() method is invoked?.

If the answer is Point.display() we have static method binding. This seems to be the right answer since after all the type of p1 is pointer to Point.

If the answer is ColoredPoint.display() we have dynamic method binding. This seems to be the right answer since after all the object pointed to by p1 is a ColoredPoint.

Semantics and Performance

Dynamic method binding does seem to have the preferred semantics. The principal argument in its favor is that if the base-class method is invoked on a derived object, it might not keep the derived object consistent.

Performance considerations favor static method binding. We shall see that dynamic binding requires each object to contain an extra pointer and also requires an additional pointer dereference when calling a method.

Language Choices

Smalltalk, Objective-C, Modula-3, Python, and Ruby use dynamic binding for all methods. Java and Eiffel use dynamic method binding by default. This can be overridden with final in Java or frozen in Eiffel.

Simula, C++, C# and Ada 95 use static method binding by default, but permit dynamic method binding to be specified (see below).

9.4.1: Virtual and Nonvirtual Methods

In Simula, C++, and C# a base class declares as virtual any method for which dynamic dispatch is to be used. For example the move method of the Point class would be written

    virtual void move (double dx, double dy) { x += dx;  y += dy; }

We shall see below how a vtable is used to implement virtual functions with only a small performance penalty.

Ada 95 uses a different mechanism for virtual functions that we shall not study at this time.

class DrawableObject {
  virtual void draw() = 0;

abstract class DrawableObject { public abstract void draw(); }

9.4.2: Abstract Classes

In most OO languages, it is possible to omit the body of a virtual method in the base class, requiring that it be overridden in derived classes. A method having no body is normally called an abstract method (C++ terms it pure virtual) and a class with one or more abstract methods is called an abstract class.

As shown on the right, C++ indicates an abstract method by setting it equal to zero; Java (in my view more reasonably) labels both the method and class abstract.

Naturally, no objects can be declared to be of an abstract class since at least one of its methods is missing.

The purpose of an abstract class is to form a base for other concrete classes. Abstract class are useful for defining the API when the implementation is unknown or must be hidden completely.

Classes all of whose members are abstract methods are called interfaces in Java, C#, and Ada 2005. Note that interfaces by definition contain no data fields.

9.4.3: Member Lookup

How can we implement dynamic method dispatch? That is, how can we arrange that the method invoked depends on the class of the object and not on the type of the variable?

The method to call at each point cannot be determined by the compiler since it is easy to construct an in which the class of the object referred at a point in the code depends on the control flow up to that point. Thus some run-time calculation will be needed and the method of choice is as follows. During execution a virtual method table (vtable) is established for any class with one or more virtual methods; the table contains a pointer for each of the virtual methods declared.

class B { // B for base
  int a;
  virtual void f() {...}
  int          g() {...}
  virtual void h() {...}
  virtual void j() {...}
} b;
class D : public B {
  int w;
          void f() {...}
  virtual void h() {...}
  virtual void z() {...}
} d;

When a class is derived from this base class the derived vtable starts with the base vtable and then

When an object is created it contains a pointer to the vtable of its class. When a virtual method invocation is called for, we follow the object's pointer to the class vtable and then follow the appropriate pointer in the vtable to the correct method.

Converting Between Base and Derived Classes

#include <stdio.h>
class B {
    int x;
    virtual void f() {printf("f()\n");}
} b, *pb;

class D : public B { public: int y; } d, *pd;
int main (int argc, char *argv[]) { B bl, *pbl; D dl, *pdl; printf ("main\n"); b = d; // b = dynamic_cast<B>(d); not for objects b = (B)d;
// d = b; type error // d = dynamic_cast<D>(b); not for objects // d = (D)b; type error
pb = &b; pd = &d; pb = pd; pb->x =1; pb = dynamic_cast<B*>(pd); pb->x =1; pb = (B*)pd; pb->x =1;
pb = &b; pd = &d; // pd = pb; illegal conversion pd = dynamic_cast<D*>(pb); // pd->y=1; seg fault pd = (D*)pb; pd->y=1; // invalid, but no error msg
#include <stdio.h> class B { public: int x; virtual void f() {}; } b, *pb; class D: public B { public: int y; } d, *pd; int main(int argc, char *argv[]) { b.x = 11; d.y = 22; pb = &b; pd = (D*)pb; pd->y=1; printf("b.x=%d d.y=%d\n", b.x,d.y); }
pb = &d; // pd = pb; type error pd = dynamic_cast<D*>(pb); pd->y=1; return 0; };

C++: On the right we see some C++ code that uses different methods to convert between a base class B and a derived class D. There are naming conventions to keep straight the properties of all the variables.

Some of the lines are commented out. These generated compile or run time errors as stated in the comment. The version as shown compiles and runs.

The first set of attempted conversions tries to assign dg to bg. Recall that the first is a global variable containing an object of the derived class and the second is a global variable containing an object of the base class. This is supposed to work (an object of the derived class can be used where a base object is expected ) and indeed it does either naked, as in the first line, or with an explicit cast as in the third. The second line is an erroneous attempt to use for objects a feature designed for pointers (see below).

Next we try the reverse assignment, which fails. As expected, we cannot use a base object where a derived object is expected. Recall that the derived class typically extends the base so contains more information.

Now we move from objects to pointers. The first two lines initialize the pointers to appropriate objects for their types. Setting the base pointer to the derived pointer is legal: Since the derived object can be considered a base object, the base pointer can point to it. Now the dynamic_cast is being used correctly. It checks at run time if casting the pointer is type correct. If so it performs the cast if not it converts the pointer to null (which is type correct, but often leads to segmentation errors, see below). We also do the sometimes risky nonconverting type cast, which here is type correct.

Next, we reinitialize the base and derived pointers to point to base and derived objects and then try the invalid assignment of the base pointer to the derived pointer, which would result in the derived pointer pointing to a base object. A naked assignment is caught by the compiler. The dynamic cast does its thing and returns the null pointer, which results in a segmentation fault when the pointer is used. The nonconverting type cast works, but the result is that the derived pointer points to a base object, which has no y component. Although, assigning to t does not cause a error (this time), it does not succeed in setting y. Instead it must be setting some other word of memory; what a scary thought! This shows the danger of nonconverting type casts.

To illustrate that we have not set y, the next frame shows a little test program. When run, we find that setting y does not effect the value of either y or x.

Finally we have the base pointer point to the derived object, which is perfectly valid. The subsequent naked assignment fails at compile time even thought it is actually a legal assignment. The dynamic_cast comes to the rescue. The test program above, when modified for this case by changing pb=&b; to pb=&d; (a one character change) works fine and does indeed set y to 1.

public class B {
    public int a;
    public void f() {System.out.println("f()");}

public class D extends B { public int y,w; public void g() {System.out.println("g()");} }
public class M { public static void main (String[] args) { B bg = new B(); D dg = new D(); System.out.println("main"); bg = dg; bg = (B)dg;
bg = new B(); dg = new D(); // dg = bg; type error // dg = (D)bg; will not convert
bg = new D(); // dg = bg; type error dg = (D)bg; } }

Java: On the right we see a Java example. Although shown here as one listing, each public class is actually a separate .java file. Java does not have C++ (really C) pointers so we don't use the variables beginning with p as we did with the C++ example above. But remember that, for objects, Java uses reference not value semantics so all the variables are in a sense pointers; in particular, an assignment statement changes the pointer not the object.

The first group of statements shows that, as with C++, we can use a derived object where a base is expected; the explicit cast is optional.

In the second group we try to use a base class where a derived class is expected. Note that is this group the base variable refers to a base object and the derived variable refers to a derived object (unlike the next group). The first line is a naked assignment and results in a compile time type error. In the second we employ a cast, which does compile (see the next group), but then generates a run time error since a base object cannot be viewed as a derived object.

The last group is perhaps the most interesting. As before the derived variable refers to a derived object; however, the base variable refers to a derived not base object. This is perfectly legal, for example, this is the state that occurred after the first group of statements was executed. It is still true that we cannot assign the base object to a derived variable (type error) with a naked assignment. However, the cast works because the error found in the second group was via a dynamic, i.e., run time, check. This time bg does refer (i.e., point) to a derived object and hence the type can be converted and the assignment made.

More C++ vs Java: Terminology


MethodVirtual member function
Static MembersSame
Abstract methodsPure virtual member functions
InterfacePure virtual class with no data members
Interface implementationinheritance from an abstract class
fun mkAdder addend = (fn arg => arg+addend);

val add10 = mkAdder 10;
add10 25
class Adder { int addend; public: Adder (int i) : addend(i) {} int operator() (int arg) { return arg+addend;} };
int main (int argc, char *argv[]) { Adder f = Adder(10); printf("f(25)=%d\n",f(25)); return 0; }

Objects vs First-Class Functions

Using an Object to Produce a First-Class Function Here is a clunky implementation of a simple first-class function via an object. The ML function mkAdder returns a function that adds the given addend. In the second frame we use mkAdder to produce add10, which we use in the third frame (it gives an answer of 35).

In the next frame is a C++ class that very cleverly does the same thing. It is tested in the next frame and the answer is again 35.

class Account { // Java
  private float theBalance;
  private float theRate;
  Account (float b, float r) {theBalance=b; theRate=r;}
  public void deposit (float x) {
    theBalance = theBalance + x;
  public void compound () {
    theBalance = theBalance * (1.0 + rate);
  public float balance () { return theBalance; }

(define Account (lambda (b r) (let ((theBalance b) (theRate r)) (lambda (method) (cond ((eq? method 'deposit) (lambda (x) (set! theBalance (+ theBalance x)))) ((eq? method 'compound) (set! theBalance (* theBalance (+ 1.0 theRate)))) ((eq? method 'balance) theBalance))))))

Using a First-Class Function to Produce an Object This time we are given a Java class that maintains a bank balance and has methods for making a deposit, applying interest, and retrieving the current balance. When you create an instance of the class you give it an initial balance and an interest rate to be applied whenever you invoke the compound method (the name is chosen to suggest compound interest).

The frame below gives a Scheme implementation using a function Account that produces another function as a result. It also uses two bangs.

Rather than give two more frames with the usage and answers, I copied the file over to access.cims.nyu.edu so we can hopefully see it work here.

9.4.4: Polymorphism

We will learn generics soon and have seen a few hints already. Some of the material from this section is discussed in our treatment of generics. The idea of generics is that you give a type variable as a parameter of a generic and then instantiate the generic for various specific types. This is some times called explicit parametric polymorphism as opposed to the subtype polymorphism offered by inheritance.

Thus generics are useful for abstracting over unrelated types, which is something inheritance does not support.

The Circle and the Ellipse: Subtype Polymorphism Gone Wrong

Every circle is an ellipse so it makes sense to derive a Circle class from an Ellipse class and, by subtype polymorphism, to permit a circle to be supplied when an ellipse is expected.

But this doesn't always work. A reasonable method to have in the ellipse derived type is to enlarge the ellipse by stretching it in two directions, parallel and perpendicular to its major axis. But if the two expansion coefficients are different a circle would not remain a circle.

Homework: CYU 31, 32, 36 (only the C++ part), 37, 38.

9.5: Multiple Inheritance

9.6: Object-Oriented Programming Revisited

An interesting read; I recommend you do so.

9.A: Effective C++ (Scott Meyers)

class String {
  char *data;
  String(const char *value) {
    if (value) {
      data = new char[strlen(value) + 1];
      strcpy(data, value);
    else {
      data = new char[1];
      *data = '\0';
  ~String() { delete [] data; \}
  ... // no copy constructor or operator=

String a("Hello"); { // introduces a local scope String b("World"); b = a; } String c = a; // Same as String c(a);

Meyers wrote two books (Effective C++ and More Effective C++) that offer suggestions on a good C++ style. I just show a very few, using material based on Prof Barrett's notes.

Constructors, Destructors, and Assignment Operators

Item 11

Meyer's item 11 states

Declare a copy constructor and an assighment operator for classes with dynamically allocated memory.

We saw an example previously, where the shallow copy performed by the C++ default copy constructor, fails to copy dynamic memory. It copies instead the pointer to the memory.

Look at the code on the right and notice that

What is needed is a deep copy constructor that copies the string as well as the pointer. This is emphasized in lab 3.

#include <stdlib.h.>
class Vector {
    int size;
    int *A;
    Vector(int s) : size(s) {A = new int[size];}

class Array { private: Vector data; // another class int size; int lBd, uBd; public: Array(int low, int high) : lBd(low), uBd(high), size(high-low+1), data(size) {} };

Item 13

Meyer's item 13 states

List members in an initialization list in the order in which they are declared.

The code on the right is flawed. The constructor for data will be passed an undefined value because size has not yet been initialized, even though it looks as though it has.

The reason is that members are initialized in the order they are declared, not in the order they are listed in the constructor.

So, to avoid confusion, when using an initialization list, you should always list members in the order in which they are declared.

int w, x, y, z;
w = x = y = z = 0

C& operator=(const C& rhs) { ... return *this; }

Item 15

Meyer's item 15 states

Have operator= return a reference to *this.

The first frame is quite common in C and C++. So you want to permit it for your classes, which is easy to do. Just make sure that your operator= function returns *this.

Item 16

Meyer's item 16 states

Assign to all data members in operator=.

If you don't have an operator=, C++ will do a shallow assignment, which is fine for simple data like integers but not for heap allocated data. This is the reason you have operator=. But, once you write operator=, C++ does nothing not even the shallow assignment for items you don't mention. So be sure to assign them all.

If this is a derived class, be sure to explicitly call the base class operator= (this applies to the copy constructor as well).

class My_Array {
  int * array;
  int count;
  My_Array & operator = (const My_Array & other) {
    if (this != &other) // protect against invalid self-assignment {
      // 1: allocate new memory and copy the elements
      int *new_array = new int[other.count];
      std::copy(other.array, other.array + other.count, new_array);

      // 2: deallocate old memory
      delete [] array;

      // 3: assign the new memory to the object
      array = new_array;
      count = other.count;
    // by convention, always return *this
    return *this;

Item 17

Meyer's item 17 states

Check for assignment to self in operator=.

Since operator= will delete the old contents of the left hand side, you must be check for the case where the client code is simply x=x;.

Wikipedia gives a good fairly generic code example, which I copied on the right. Note that it makes sure to return *this

The principle followed is to proceed in this order

  1. Acquire new resources and copy needed value from old resources.
  2. Release old resources.
  3. Assign the new resources' handles to the object.

Start Lecture #12

Chapter 8: Subroutines and Control Abstraction (continued)

8.4: Generic Subroutines and Modules

What Is the Problem We Are Trying to Solve?

Think of a routine to sort an array of integers using heapsort. Now imagine you need a routine to sort an array of

The sorting logic remains the same in all cases. For the first three cases, you just want to change the type of some variables from int to float to string (assuming < for strings compares the strings not the addresses). For the last case, you need to change the definition of < as well.

Generics are good for this.

Generics are perhaps most widely used for containers, structures that contain items, but whose structure doesn't depend on the type of the items contained. Examples include, stacks, queues, dequeues, etc. Normally, the containers are homogeneous, that is, all of the items in a given container are of the same type. We don't want to re-implement the container for each possible item type.

Why Isn't Inheritance Good Enough?

Inheritance is also used for containers, indeed, containers formed one of our chief inheritance examples (lists, queues, and dequeues).

The the comments that follow are inspired by words in section 9.4.4 (part of the chapter on inheritance). I deferred it to here since we did inheritance before generics so could better compare the two when doing generics.

When studying inheritance, we considered a gp_list_node class and derived from that int_list_node, float_list_node, etc. This seemed pretty good since all the list properties (predecessor, successor, etc) were defined in the gp_list_node class and thus inherited by each *_list_node class.

int_list_node *q, *r;
r = q->successor();  // error: type clash

gp_list_node *p = q->successor(); cout << p.val; // error: gp_list_nodes have no val
r = (int_list_node*) q->successor(); cout << r.val // OK, but scary
r = dynamic_cast<int_list_node*> q->successor() cout << r.val // OK if have vtable
int_list_node* successor() { return (int_list_node*) gp_list_node::successor(); }

However, problems remain. The first frame represents what I would consider natural code for marching down a list built of int_list_nodes. It fails because successor() returns a gp_list_node*, which cannot be nakedly assigned to an int_list_node*.

The second frame shows the result of quickly applying the motto fix it now; understand it later.

The third frame shows a real fix. However, a nonconverting type cast is always scary.

The fourth frame also gives a real fix; one that is not so scary. However, it wouldn't work for the specific code we had since there were no virtual functions and hence no vtable. We certainly could have added a virtual function and, more importantly, a large project would very likely have one.

Clients of our class would not be happy with this awkward solution and hence we would likely revise int_list_node to redefine successor() as shown in the next frame.

This last solution seems the best of the possibilities, but is far from wonderful since we needed to redefine methods whose functionality did not change. We have lost some of the reuse advantages of inheritance!

template<class V>
class list_node {
  list_node<V>* prev;
  list_node<V>* next;
  list_node<V>* head_node;
  V val;
  list_node()  // SAME code as in orig
  list_node<V>* predecessor() { // SAME code as in orig
    if (prev == this || prev == head_node) return 0;
    return prev; }
  list_node<V>* successor() {...}
  void insert_before(list_node<V> new_node) {...}
template<class V>
class list {
  list_node<V> header;
  list_node<V>* head() {...}
  void append(list_node<V> *new_node) {}
typedef list_node<int> int_list_node;
typedef list<int> int_list;
int_list numbers;  // list<int> without typedef
int_list_node *p;
p = numbers.head();
p = p->successor();  // no cast required!

The Solution with Generics On the right we see the solution with generics. The classes list_node and list have been parameterized with the type V. Think of instantiating this with V=int and then instantiating it again with V=float.

With V=int we see that the first public field becomes int val, which is perfect (i.e., is exactly what it was for int_list_node previously).

The prev field is of type list_node<int>*, which is reasonably close to int_list_node.

In order to permit the clients to use int_list_node, they need only include a typedef as we have done.

Finally, note that the code to march down a list is the natural linked list code. All in all this use of generics turned out quite well.

Back to the Present (section 8.4)

Having shown by an example that inheritance, which we have already studied, does not by itself eliminate the need for generics, we turn our attention to a proper treatment of generics itself.

We just looked at a linked list example where the type of the data items (the val field) can vary. For now just think of int and float. If we were to explicitly declare the data item, as is needed in languages like Pascal and Fortran, then we would need separate routines for ints and floats. The difficulty is not in producing the multiple versions (copy/paste does most of the work), but rather in keeping them consistent. In particular, it is a maintenance headache to need to remember to apply each change to every copy.

We could have the lists contain pointers to the data item rather than the items themselves (cf. reference vs. value semantics). This would be the likely solution in C. Of course a pointer to an int, i.e., an int* is not type compatible with a float*, so we play the merry game of casts and kiss off the advantages of compile time type checking. An alternative in C is to use the macro facility to produce all the different versions. As we have seen when studying call-by-name, macro expansion requires care to prevent unintended consequences.

We could use implicit parametric polymorphism as we have seen in Scheme and ML. The disadvantage of the Scheme approach (shared with many scripting languages) is that type checking is deferred to run time. ML is indeed statically typed, but this does cause the compiler to be substantially slower and more complicated (not noticeable for the tiny programs we wrote). Moreover, ML has been forced to adopt structural type equivalence instead of the currently favored name equivalence. Finally, we saw early in the course that ML's treatment of redeclaration differs from that chosen by most languages supporting the feature.

Models Used for Generics in Various Languages
CMacros (textual substitution) or unsafe casts
AdaGeneric units and instantiations
C++, Java, and C#Templates
MLParameteric polymorphism, functors

In this section we will study explicit polymorphism in the form of generics (called templates in C++). The different models used for generics are summarized in the table on the right.

A Little History for Ada, C++, C#, and Java Generics

Ada generics were in the original design and Ada 83 implementations included generics. Generics were envisioned for C++ early in its evolution, but only added officially in 1990. C# generics were also planned from the beginning but didn't appear until 2004 (release 2.0). Java, in contrast, had generics omitted by design; they were added (to Java version 5) only after strong demand from the user community.

Parameterizing Generics

As we have stated a generic facility is used to have the same code apply in a variety of different circumstances. In particular, it is to be applied for different values of the parameters of the generic. These parameters are called the generic parameters.

Let first consider a simple case. If we had a generic array, the natural parameters would be the array bounds and the type of the components. Indeed the definition of a (1-dimensional, constrained) array in Ada is of the form

    <array_name> : array <discrete_subtype> of <component_type> ;
The discrete subtype is characterized by its bounds. Higher dimensional arrays simply have more <discrete_subtypes>. (Ada also has unconstrained array types, that we are not discussing).

So for a given array declaration, we supply the index bounds and the type of the components; these are the parameters.

An (ordinary) subprogram is also generic in that it can be applied to various values for its arguments; those arguments are the parameters.

Parameters for Various Generic Facilities (incomplete).

arraybounds, element type
subprogramvalues (the arguments)
Ada generic packagevalues, types, packages
Ada generic subprogramvalues, types
C++ class templatevalues, types
C++ function templatevalues, types
Java genericclasses
ML functionvalues (including other functions)
ML type constructortypes
ML functorvalues, types, structures

In all cases it is important to distinguish the parameters from the construct being parameterized. For example, we parameterize a subprogram by supplying values as parameters.

Turning to the new constructs, we see that in many cases types can be parameters. So an Ada generic subprogram for sorting would take a type parameter and thus can serve as an integer sorter, a float sorter, etc.

There is a weird (mis)naming convention you should understand and sadly must deal with. Recall from above the C++ phrase template<class V>. The requirement is that V must be a type. Moreover, there is an alternative syntax template<typename V> that is perfectly legal and completely descriptive. However, probably from habit, it is not normally used. I found a clear explanation here.

   type T is private;
procedure swap (A : in out T; B : in out T);

procedure swap (A: in out T; B : in out T) is Temp : T; begin Temp := A; A :=B ; B := Temp; end swap;
with swap; procedure Main is procedure SwapInt is new Swap(Integer); X : Integer := 3; Y : Integer := 4; begin SwapInt(X,Y); end Main;

A Generic Swap in Ada

On the right is the familiar three part Ada example: specification, body, use.

We offer to clients a generic procedure Swap; the genericity is parameterized by a type T (private is explained below).

The body implements Swap, which is trivial. Note that here the type T is treated as a normal type.

The client instantiates a new procedure SwapInt, which is the generic Swap, with the generic class T replaced by the specific class Integer. The instantiated procedure SwapInt is used just as if it were defined normally.

What about the private in the specification? The purpose of this private (or of other possibilities) can be seen from two viewpoints: the client or the author of the body. From the author's viewpoint, private is saying what properties of T can be used. The private classification means that variables can be declared of type T and can be assigned and compared for equality. No other properties can be assumed. For example, the author cannot ask if one type T value is less than another (so we couldn't write a maximum function). A more restrictive possibility would be limited private in which case assignment and equality comparison are forbidden as well. So, from the author's viewpoint, the more restrictive the requirement, the less functionality is available.

The more the author is constrained, the less the client is constrained since whatever functionality the author can assume about type T, the client must guarantee his specific type supplies.

   type T is private;
   with function ">" (X,Y : T) return Boolean;
function max (A,B : T) return T;

function Max (A,B : T) return T is begin if A > B then return A; end if; return B; end Max;
with Max; procedure Main1 is function MaxInt is new Max(Integer,">"); X : Integer; begin X := MaxInt(3,4); end Main1;

A Generic Max in Ada

As just mentioned, the generic type parameter cannot, by default, be assumed to supply a > operator. So to implement a generic max, we need to pass in the > as a parameter.

The first frame again has the specification. We see that the second generic parameter is >. Don't read too much into with; it is there to prevent a syntactic ambiguity.

Frame 2 is a standard maximum implementation.

In the last frame we see that we are supplying > for >. Since this is so common, there is extra notation possible in the specification to make it the default. In any event, when MaxInt is instantiated the compiler checks to see that there is a unique operator visible at that point that matches the signature in the specification.

As a not-recommended curiosity, I point out

    function MinInt is new Max(Integer, "<");

   MaxSize: Positive;
   type ItemType is private;
package Stack is
   procedure Push(X: ItemType);
   function Pop return ItemType;
end Stack;

package body Stack is S: array(0..MaxSize-1) of ItemType; Top: Integer range 0..MaxSize; procedure Push(X: ItemType) is begin S(Top) := X; Top := Top+1; end Push; function Pop return ItemType is begin Top := Top-1; return S(Top); end Pop; begin Top := 0; end Stack;
with Stack; procedure Main2 is package MyStack is new Stack(100,Float); X: Float; begin MyStack.Push(6.5); X := MyStack.Pop; end Main2;

A Generic Stack Package in Ada

Here we see a generic parameter that is not a type. The parameter MaxSize is a value, in this case a positive integer. The third possibility in which a package is a parameter to another package is not illustrated.

The specification has the generic prefix and then looks like a stack package. It offers the standard push and pop subroutines. The MaxSize parameter is not used here; it is needed in the body.

The package body is completely unremarkable. Indeed, if you just substitute some positive value for MaxSize and some type for ItemType, it is a normal stack package body. It can certainly be criticized for not checking for stack overflow and underflow.

The client code is also not surprising, but does have a curiosity. MyClient.Push looks like OOP. It isn't really since MyClient is a package not an object, but it does look that way. Indeed, you could remove both generic parameters, changing the specification and body to use say 100 and float and still create several new packages MyStack1,   Mystack2,   etc. Then you would have MyStack1.Push(5.2);,   X:=MyStack2.pop;,   etc.

template<typename itemType, int maxSize>
class stack {
     int top;
     itemType S[maxSize];
     stack() : top(0) {}
     void push (itemType x) {
         S[top++] = x;
     itemType pop() {
         return S[--top];

#include <iostream> #include "stack.cpp" int main(int argc, char *argv[]) { stack<float, 50> myStack; float x; myStack.push(4.5); x = myStack.pop(); }

The Same Generic Stack Package in C++

On the right is the C++ analogue of the Ada stack package above. The first frame is the body (implementation), the specification (interface) is not shown. The C++ body is clearly much shorter than the Ada version. The only functionality missing is the range specification for Top; the real reason the lengths are so different is the relative emphasis the two languages place on readability vs conciseness.

The client code in the next frame is almost the same as the Ada version.

public class Stack<T> {
  final int maxSize;
  int top;
  T[] S;
  public Stack(int size) {
    maxSize = size;
    top = 0;
    S = (T[]) new Object[maxSize];
  public void push(T x) {
    S[top++] = x;
  public T pop() {
    return S[--top];

public class MainClass { public static void main(String args[]) { Stack<Float> myStack = new Stack<Float>(10); Float X; myStack.push(3.2F); X = myStack.pop(); } }

The Same Generic Stack Package in Java

There is a difference in the Java implementation. The generic parameters must be classes; in particular the integer maxSize cannot be specified as a generic parameter. Instead we pass the size as an argument to the constructor. Also, as usual, the reference semantics of Java means that declaring a stack only gives a reference so we need to call new.

The biggest change is that Java does not permit us to say new T so we must produce an Object and cast it to T using an unsafe cast.

The client code has a change as well. Note the capital F in Float; this is a class, rather that the primitive type float, which would be illegal. An experienced Java programmer would use a different implementation.

public class Stack<T> {
   readonly int maxSize;
   int Top;
   T[] S;
   public Stack(int size) {
      maxSize = size;
      top = 0;
      S = new T[m_Size];
   public void Push(T x) {
      S[top++] = x;
   public T Pop() {
         return S[top--];

public class MainClass { public static void Main(String args[]) { Stack<float> myStack = new Stack<float>(10); float X; myStack.push(3.2F); X = myStack.pop(); } }

The Same Generic Stack Package in C#

C# looks like Java, but there are differences. In a sense C# is between C++ and Java. The keyword final has become readonly and, in the client code, Main is capitalized. These are trivialities. Much more significantly two of the Java quirks are missing. We now write new T directly, eliminating the unsafe cast and the client can use the primitive type float rather than a class that acts as a wrapper (and increases overhead).


In summary, we can say that for simple cases like the above stack, the C++, C#, Java, and Ada solutions are basically the same from the programmer's viewpoint (with the exception of no primitive types or new Tin Java). We shall see that the implementations are different.

8.4.1: Implementation Options

In Ada and C++, each instantiation of a generic is separate. So

stack<float,50> myStack1 and stack<int,50> myStack2
will likely not share code for the push and pop routines. Indeed it is not clear if two instantiations with the same generic parameters will share code. This rather static approach may look similar to a macro expansion as for example in C. However

In contrast to the separate instantiation characteristic of C++ and Ada, all instantiations of a given Java generic will share the same code. One reason this is possible is that, due to its reference semantics, all the elements are of the same size (a pointer). Another reason is that only classes, and not primitive types or values, can be used as generic parameters. In the next section, we will see that the strong compatibility requirements of Java have limited the power of its generics facility.

The C# language, like Java, has much code sharing, but without the limitations caused by Java's type erasure implementation described below. C# does permit primitive types as generic parameters. A C# generic class does share code between all instantiations using classes as parameters (the only possibility for Java), but produces specialize code for each primitive type instantiated. As with Java, the C# generic parameters must be types, not values.

Java Type Erasure

The designers of Java version 5 very much wanted to maintain strong compatibility with previous versons of Java that did not include generics. Indeed, the compatibility requirements were not only that legacy Java would run together with Java 5 code, but also that the JVM would not need to change. As a result they implemented generics using type erasure.

The idea of type erasure is, as the name suggests, that the generic type is, in essence, erased from the generic class. First, the compiler removes the <T> from the header. Then all occurrences of T in the generic body are replaced by object, the most general class in Java. Finally, the compiler inserts casts back to T whenever an Object is returned from a generic method.

These casts are reminiscent of the best of the non-generic solutions for a linked-list that we discussed previously. A significant advantage of type erasure over the non-generic solution is that the generic-routine programmer does not have to write the casts and the generic-routine reader does not have see them.

Type erasure has its own disadvantages. For example, a Java Class C<T> cannot execute new T. This is a serious limitation, but the Java 5 designers felt that the resulting strong compatibility was worth it.

Another consequence of type erasure is that C<T1> and C<T2> do not produce different types. Instead, they both yield the raw type Gen.

The above discussion assumed only one generic parameter. However, multiple generic parameters are permitted and the comments made generalize fairly easily.

C# Reification

First, for those like me who didn't know what reify meant the definition from the Webster online dictionary is to regard (something abstract) as a material or concrete thing .

In C# each instantiation of Class C<T> with a different T yields a distinct type. I suppose this matches the definition of reification by having the something abstract be the parameterized type and having the concrete thing be the new, distinct type. With each instantiated type (having distinct generic parameter values) as a different type, C# reification does not lead to the restrictions mentioned above for type erasure. (Don't forget that, in legacy Java, generics were omitted by design and the Java 5 implementers had extremely strong compatibility constraints.)

   type T is private;
   with function "<"(X,Y:T) return Boolean;
function Smaller (X,Y:T) return T;

function Smaller (X,Y:T) return T is begin if X<Y then return X; end if; return Y; end Smaller;
with Smaller; with Ada.Text_IO; use Ada.Text_IO; with Ada.Integer_Text_IO; use Ada.Integer_Text_IO; with Ada.Float_Text_IO; use Ada.Float_Text_IO; procedure Main is type R is record I: Integer; A,B: Float; end record; R1: R := (1, 0.0, 0.0); R2: R := (1, 2.0, 3.0); R3: R; function "<"(X,Y:R) return Boolean is begin if X.I < Y.I then return True; end if; if X.I > Y.I then return False; end if; return (X.A+X.B)<(Y.A+Y.B); end "<"; function SmallerR is new Smaller(R,"<"); begin R3 := SmallerR(R1,R2); Put ("The smaller is: ("); Put (R3.I); Put (", "); Put(R3.A); Put (", "); Put(R3.B); end Main;

8.4.2: Generic Parameter Constraints

Let us restrict attention to the case of just one generic parameter as the generalization to multiple parameters is straightforward. Also, let us consider only the case of a type parameter since those are permitted in all the languages (in Java the type must be a class not a primitive type).

Generic Parameter Constraints in Ada

Recall that the Ada generic Stack package began

        MaxSize: Positive;
        type ItemType is private;
    package Stack is
and recall that a private type can only be assumed to support, assignment, equality testing, and accessing a few standard attributes (e.g., size). In place of private a few other designations can be used, but they do not come close to giving all useful restrictions on (or, equivalently, requirements of) the type.

We mentioned using with to add other requirements on the type and mentioned that, given the appropriate definition, we could compare records containing one integer and two floats, by first comparing the integers and, if there is a tie, comparing the sum of the floats. On the right is an implementation, in the usual three-part Ada style. Note, in particular the overload resolution of < in the instantiation of the generic Smaller into the specific SmallerT.

Generic Parameter Constraints in C++

The C++ generic programmer can omit explicit constraints and write a header as simple as

    template<typename T>
    void sort(T A[], int A_size) {...}
Then, when the generic component is instantiate, the compiler checks to ensure that the supplied type has all the properties used in the generic body. This is certainly concise, but does sacrifice clarity in that users of the generic component, must themselves ensure that the type they supply has all the needed properties. Worse, the type supplied might indeed supply the property, but the default property may not do what the user wanted and expected.

For these reasons, many users shun implicit constraints and make the requirements explicit. The Ada technique above can be employed: the comparison operator can be specified as a method of type T. However, rather than needing to explicitly list all the operators needed, the Java/C# method below can also be used in C++.

Generic Parameter Constraints in Java and C#

Java and C# make use of inheritance to specify the needed constraints for generic parameters. A good example is sorting

    public static <T extends Comparable<T>> void sort(T A[]) {
This generic header says that the sort routing will use type T and that T is required to have all the methods found in the Comparable interface. (Recall that an interface has only virtual methods). In this case Comparable is a Java standard interface and contains CompareTo, which compares two values returning -1, 0, +1 for the three cases of <, =, and >. The generic sort routine then contains a line like
    if (A[i].compareTo(A[j]) ≥ 0)
which holds precisely if, according to type T, A[i]≥A[j].

In other examples the interface the parameter T extends, will be user defined and will contain several virtual functions, all of which any type used to instantiate T is required to have.

C# has the same concept, but with slightly different syntax.

8.4.3: Implicit Instantiation

All four languages we studied require explicit instantiation for generic packages/classes. However, they differ for generic routines.

In Ada, a generic routine must be explicitly instantiated. Our most recent Ada example illustrated the instantiation of the generic routine Smaller into the specific routine SmallerR by replacing the generic type T with the specific type R.

In contrast C++, C#, and Java use implicit instantiation. That is they treat the generic version as an overloaded name that is resolved at usage. Thus after writing a generic sort routine, the programmer simply invokes it with arguments of type R and the system automatically (implicitly) instantiates a concrete version for type R (and for C# and Java has it share code with other instantiated versions).

8.4.4: Generics in C++, Java, and C#

Some of the material from this section (including the CD portion) has been incorporated in the above. The rest is being omitted.

8.A: Generics in ML

ML generics are called functors (a term from mathematics). You might ask, given the parametric polymorphic functions for which ML is justly famous, and also the type constructors, which we have not seen, why are generics needed at all?

8.B: Iterators and the C++ Standard Template Library STL

Iterators are entities used for accessing (iterating over) the elements of a container (list, array, tree, queue, etc). The C++ STL is a large collection of generic classes used mostly for containers. It also provides a large collection of Iterators.

The STL makes little use of inheritance.

Homework: CYU 27, 29, 30, 31.

Homework: Using the code in the notes for section 8.4.2, produce a generic sort in Ada (a trivial O(N2) sort, like bubble sort is fine). Instantiate it to sort type R items using the integer as the primary key and the sum of the floats to break ties.

Start Lecture #13

8.5: Exception Handling

Definition: An exception is unexpected/unusual condition that can arise during program execution, and that cannot be handled easily in the local context in which it occurs.

Broadly speaking exceptions come in two categories.

  1. Predefined exceptions are detected automatically by the language runtime system. Examples include
  2. User defined exceptions are raised explicitly by the program. When we studied modules, the better Ada queue implementation included QueueFull and QueueEmpty exceptions that were defined in the code and explicitly raised when the appropriate conditions occurred.

The programmer writes exception handlers (for either predefined or user-defined exceptions). The handler is invoked when the exception is raised and can specify remedial action or can orchestrate an orderly shutdown.

Exceptions can be stored and re-raised later.

Handling Errors Of course good programs check for possible errors. What should we do when we find an error (EOF when looking for data; non-existent file when attempting an open, etc)?

  1. We could return a special value that the caller checks for, in place of a normal returned value.
  2. Return an extra status value. This is conceptually similar to the first solution.
  3. Have the caller pass a closure that is to be invoked in case of an error

The first two are not lovely; the third is discussed at the end of this lecture (Scheme continuations).

Exception mechanisms incorporated in the language aid the programmer in producing clear code and ease the task of catching errors.

When an exception occurs in a monitored block of code, the handler for that exception is invoked and is executed instead of the yet-to-be performed portion of the monitored block.

What Do Handlers Do? Typically a handler does one of three things.

  1. Ideally a handler, will compensate for the exception so that the program can recover and continue. Below we see an Ada program that recovers from bad input by printing a message and having the user re-input the value.
  2. If the local block cannot recover, it should clean up its local action and re-raise the exception so that it will propagate back the dynamic chain to a handler that can recover.
  3. If the exception cannot be repaired at all, at least a helpful message, possibly plus data, can be printed to help in debugging.

Why the Dynamic (not Lexical) Chain? The reason the exception propagates back the dynamic chain rather that going to a higher lexical scope is that exceptions are runtime phenomena and it is the dynamic execution that produced the values causing the exception.

8.5.1: Defining Exceptions

Some Generalities

We have seen/will see examples of exceptions being triggered and being caught in Ada, C++, Java, and ML. In all cases there are three parts to the exception handling mechanism.

  1. A lexical region of the program.
  2. A specific list of exceptional conditions.
  3. A set of actions.
If one of the exceptional conditions occurs while the program is executing the indicated region (or other code invoked—directly or indirectly—by this region), then control transfers to one of the specified actions. When the chosen action completes control returns to the end of the indicated region.

In the better Ada queue example, the lexical region is a begin block with three queue operations and a simple if-then-else statement, the exceptional conditions are the QueueFull and QueueEmpty conditions, and the action list consists of two Put_Line instructions. Although, neither QueueFull nor QueueEmpty can be triggered by any statement in the begin block, both can occur while executing the queue operations invoked in the begin block.

Defining Exceptions in Ada

As mentioned above, the exceptional conditions can be predefined in the language or user-defined by the programmer. Ada 95 has four predefined exceptions that are declared in the package Standard, all of which indicate that something has gone seriously wrong.

  1. Constraint_Error: Some value is out of range, which includes a value not in the domain of an operation (e.g, a divisor of zero).
  2. Program_Error: An attempt to violate the control structure has occurred (and has not been detected at compile time). For example if a Function reaches the end without hitting a return statement.
  3. Storage_Error: The system has run out of space.
  4. Tasking Error: Some problem with concurrency.

In addition the package Ada.IO_Exceptions defines eight more: Status_Error, Mode_Error, Name_Error, Use_Error, Device_Error, End_Error, Data_Error, and Layout_Error. Nearly all represent error conditions, only End_Error, which indicates that an input instruction has encountered an end of file, can be considered an expected occurrence.

We shall see that Ada is unusual in that the raise statement gives the handler no information other than the exception name. Other languages permit additional data to be passed to the handler as a parameter. In Ada, the handler itself can obtain other information, as we show below. Moreover, instead of the simple raise statement the programmer can employ raise_exception, a procedure that permits more information to be sent to the handler.

with Ada.Integer_Text_IO; with Ada.Text_IO;
use Ada.Integer_Text_IO; use Ada.Text_IO;
function Get_Data return Integer is
  X: Integer;
      return X;
      when others =>
        Put_Line("Need integer; try again.");
  end loop;
end Get_Data;

Builtin Exception Example—Checking Input Data: The simple example on the right illustrates a few points about exception handling in Ada.

User-Define Exception Example: We have already seen user-defined exceptions in Ada when we wrote the better queue example. After reviewing that example, we should note the following points about the use of exceptions.

The example asserts that we have declared two exceptions. This is not strictly true as exceptions are not members of a type: for example, there are no exception variables.

when Event: others =>
  Put("Unexpected Exception: ");

However Ada has a related concept, Exception_Occurrence, which is a type. Consider the code fragment on the right. The variable Event is called a choice parameter. A variable declared in this unusual way is of type Exception_Occurrence and contains a great deal of information about the exception that has just occurred.

The code uses two system-supplied functions that accept as arguments variables of this type. Another such function is Exception_Information, which unlike the one-line Exception_Message, gives details, sometimes including a trace back. An Exception_Occurrence, including all its information, can be saved using other procedures and functions. The package Ada.Exceptions has all the machinery.

Another point shown in the code is the raise statement used without an exception name. This usage re-raises the same exception in the dynamically previous scope.

try {
  if (...) throw 8;
}catch (int e){
  cout<<"caught "<< e <<"\n";

#include <iostream> class zerodivide { public: zerodivide() {} }; int main (int argc, char *argv[]) { try{ throw zerodivide::zerodivide(); }catch(zerodivide z){ std::cout<<"caught"; } }

Defining Exceptions in C++

Although the runtime time behavior is similar (exceptions are raised and caught), C++ and Ada do have real differences in exception handling. One difference is that exceptions are only raised explicitly by a throw statement; there is no analogue of the Ada builtin exceptions. Flash: Apparently, there is at least one builtin exception namely bad_cast, which is thrown by our friend dynamic_cast<T>, when the type T is a reference (we have used it only when T was a pointer, in which case it does not throw an exception).

As the top example on the right illustrates, the Ada begin block becomes a try block in C++; raise becomes throw; and when becomes catch. However, the details differ significantly.

In Ada you declare exceptions and raise them. In C++ you throw any object; the top example on the right throws an integer. Throw and catch act a little like a subroutine call in that the object thrown becomes the parameter of the catch.

Often you declare a unique class for each type of exception you wish to throw; the second example shows the class zerodivide. Note that nothing in the class associates it with exception handling. Since we do not have an object of class zerodivide available, the throw in this case is a call to the constructor, which produces the needed object.

It is common usage in cases like this where the parameter z is not used in the catch for it to be omitted and the line to the right is replaced by }catch(zerodivide){.

A catch statement for a class C also catches throws of any object in a class D derived from C. Although both our examples had only one catch block for each throw block, one can have multiple catches. In that case the first one that matches is chosen. Thus, if you have a catch for class C and another catch for class D derived from C, you must place the catch for D first (otherwise D exceptions would be caught by the C handler). The most general catch statement possible is written catch(...) and will catch any exception.

Another difference between Ada and C++ exceptions is that C++ exceptions are bona-fide types.

A subroutine header in C++ can optionally include a list of exceptions that may propagate out of the subroutine. If the list does appear and an exception occurs that is not caught locally, the program is terminated. If the list does not appear, all exceptions not caught locally will propagate. Compare this with the Java behavior described below.

public class Zd extends Exception {
  public Zd() {};
  public static void main(String[] args) {
    try{ ...
      if (...) throw new Zd();
    }catch(Zd z){

Defining Exceptions in Java

The Java model is close to C++ as seen by the example on the right with its try. throw, and catch. But of course there are differences.

Checked and Unchecked Exceptions: Java makes an important distinction between checked and unchecked exceptions. The basic idea is that the program should be aware of checked exceptions, and either handle them if they occur or announce their possibility to callers of the method. Unchecked exceptions are those from which it is not reasonable to expect the program to recover and methods are permitted to ignore the possibility of their occurring. This distinction is specified via the class hierarchy as follows.


All classes that are thrown and caught, must be subclasses of the system class throwable, which has derived classes Exception and Error. The former has a derived class RuntimeException

import java.io.*
public void f() throws FileNotFoundException {
  FileInputStream fis =
   new FileInputStream("/tmp/x");

The method f on the right needs the throws clause because the constructor for FileInputStream can throw the checked exception FileNotFoundException and hence f must either catch that exception or announce it in the header as I did. Failure to do so would result in a compile time error.

exception DivideByZero
fun f (i,j) =
    if j = 0
    then raise DivideByZero
    else i div j;
f(6,2) handle DivideByZero => 42;
f(6,0) handle DivideByZero => 42;

- [opening /tmp/handle.sml] exception DivideByZero val f = fn : int * int -> int val it = 3 : int val it = 42 : int

Defining Exceptions in ML

Again the runtime model is similar, but with differences. Perhaps the major difference is that ML is an expression language so a handle is attached to an expression and the handle itself must return a value of the same type as the expression it is attached to.

On the right is a simple example.

It is possible for the exception statement to have parameters, in which case the corresponding raise statement must supply arguments.

8.5.2: Exception Propagation

As we have seen, when an exception occurs, the local handlers are checked in order and control is transferred to the first one that matches (remember that a handler for a C++ or Java class also handles exceptions of any derived class).

If the local environment doesn't catch an exception, in most cases it propagates back up the dynamic chain to the caller. The exception is that, if a C++ routine lists exceptions that can propagate, and a different exception arises and is not caught locally, then the program terminates.

If the exception propagates to a routine outside the scope of the exception declaration, then the exception cannot be named and hence can be caught locally only with a catch-all statement.

Typically, if an exception propagates all the way out of the main program, the job terminates. A question arises what to do with a multi-threaded job if one of the threads has an exception propagate all the way out. In Ada and Java, just the thread terminates; in Modula-3, the entire job terminates; and C++ does not specify.

Handlers on Expressions

As we have seen above, expression languages like ML attach exception handlers to expressions not statements. These expressions propagate back just as in C++, Ada, and Java. For example consider the following statement inside a function M(y)

    f(x) handle except1 => 100;
Assume the evaluation of f(x) invokes a function g, which in turn invokes h and the execution of h raises except. If there are no handlers for except in f, g or h, the exception will propagate back to the handler attached to the invocation of f (which you recall is inside M(y)) and the value 100 will be supplied.

Cleanup Operations

As mentioned several times above, if an exception occurs for which there is no matching handler locally, the exception propagates back up the dynamic chain (i.e., the call chain). As this occurs, the various stack frames are reclaimed and saved registers are restored just as if the called routines returned normally.

Some languages offer support for users to do their own, additional cleanup. For example, if heap memory was allocated before the exception, when/how can it be freed?

Java, C#, Python, and Modula-3 have a fairly clean solution. Associated with a try block is an optional finally clause, which is always executed when the try block ends, whether the block terminates by

Thus cleanup code for the try block can be placed in the finally clause with confidence that it will be executed (there is a corner case involving multi-threading).

C++ guarantees that as an exception is unwinding the dynamic chain and leaving scopes, all objects declared in those scopes have their destructors called. Programmers often use such desctructors to place other clean-up code (e.g., closing files).

Ada has a concept of controlled type for which finalizations are automatically invoked. These types can be used for cleanup in a manner similar to what we saw for C++.

8.5.3: Implementation of Exceptions

As the exception propagates back the call chain, the stack frames must be deleted as mentioned above. Also required is that, in each frame, a check is made to see if there is a matching handle. A simple implementation is not hard; the book discusses a more sophisticated approach that we are skipping.

Exception Handling without Exceptions

We will briefly discuss three possibilities: statement labels, call-with-current-continuation, and setjump/longjump.

Statement Labels: Some older languages permitted, what now might be considered unnatural acts to be performed with statement labels to permit programs to escape from deeply nested contexts in an unstructured way.

call-with-current-continuation: Scheme has a quite powerful construct named call-with-current-continuation or call/cc. (See also an earlier section on continuations.) The format is (call-with-current-continuation f), where f is a function.

Assume we are in a function g and execute the above call/cc. The result is to call f passing it a closure representing the current environment. The closure is packaged as a function, call that function CC. If f ever invokes CC, the effect is that the original function g is resumed right after the call/cc. So far this looks like a very fancy way to call f.

The key is that f can call another function h and pass it CC. If h calls CC, return goes back to g bypassing f! This ability to abandon intermediate functions is reminiscent of exceptions and, with clever coding, call/cc can be used to simulate try-raise-catch.

Indeed, continuations are quite powerful and can simulate a number of different control constructs.

Continuations are also available in ML.

if (!setjump(buffer) {
   protected code
} else {
   /* handler */

setjump/longjump: Shown on the right is a familiar (to avid C programmers) pair of library routines setjump and longjump. The argument to setjump is a buffer to store the current state (a kind of temporary closure; unlike a real closure, the information saved by setjump is lost once the protected code completes). The call to setjump returns zero so the then branch is taken and the protected code executed.

If, within the protected code, longjump(buffer,v) is executed, setjump will magically return again, but this time with a value of v, which is typically non-zero. Hence the handler will be executed.

Thus setjump-longjump-else acts in a manner similar to try-throw-catch.

If you have taken OS, the ability of longjump to return twice (with different values) even though it was called only once might remind you of the Unix fork() system call.

Homework: CYU 33, 36.

Homework: Write a program in your favorite language in which f calls g, g calls h, an exception is raised in h, and the exception is handled in f.

Start Lecture #14

Remark: The final exam will be held in a DIFFERENT room on the same floor as the class room. 5-6:50 CiWW 102 Wed 23 DEC 2009.

Chapter 12: Concurrency

Remark: This is a very serious chapter, containing much more material that we are covering.

When discussing flow of control, we always assumed that there was only one! That is, you could say something like

We are executing instruction X. If a zero-divide exception occurs, we next execute this handler. Otherwise we follow the semantics of the language (goto, while, if, etc).
The key being that at any time only one instruction is executing.

To use the technical term, we have been studying sequential programs.

Now we wish to briefly consider concurrent programs, i.e., programs for which at any time more than one instruction may be eligible for execution.

Definition: A program is called concurrent if it may have more than one active execution context—more than one thread of control.

Definition: We refer to each execution context as a thread.

Reasons for Concurrency

  1. Suggested by the logical structure of the problem. Consider a Google-like program that is search some database for relevant items and display the items in rank order, more important items first. So we have a database activity of searching and finding hits, a ranking activity (deciding importance of each hit), and a display activity. Since the search can be long we want to start displaying as soon as the first hit is found. The logical structure suggests that the display activity should be running at the same time as the search; perhaps the ranking should be a third, concurrent activity. For more examples see 12.1.1
  2. Increasing performance. You need to search two databases and have two CPUs available. Rather than searching the databases one at a time, you give each CPU a different database to search and combine the results obtained. There is a very long history of such uses of concurrency in large-scale computing. However, multiple CPUs are now commonplace in laptops and desktops (but not netbooks) so the opportunities are increasing.
  3. Interacting with multiple physical devices. This situation is commonplace with embedded systems.

Challenges with Concurrency

  1. Communication. There are two parts to this question.
  2. Non-deterministic. There are many possible execution orders. The outcome can depend on the specific order that occurred and this order can easily change when the program is run again. For just two threads with N and M instructions in each there are C(N+M.N) possible interleavings. For N=M=6, this is 665,280. For N=M=10, this is 670,442,572,800. So we can't test all the possibilities.
  3. Synchronization. In order to tame the massive non-determinism, it is often necessary to synchronize the threads, i.e to restrict the possible interleavings. One example, which we will examine, is mutual exclusion, a simple case of which is requiring exclusive access to certain sections of the program. That is, if process A is in one of these critical sections, no other process may enter the section. Another example of coordination is a barrier, i.e. a point in the program that no thread may pass until all the threads have reached it.

Models of Concurrency

The model chosen answers the question How do the threads communicate?. Two models are well studied: shared memory and message passing.

Shared Memory In the shared memory model, multiple threads have read/write access to certain variables, which can be used to pass information from one to the other.

We will see that the possibility of race conditions (defined just below) forces users of shared memory to study techniques for synchronizing the threads.

Message Passing In the message possible model, threads have no shared state. Instead they communicate via sending and receiving messages to and from each other.

12.1: Background and Motivation

Race Conditions

Definition: A race condition occurs when two (or more) processes or threads are about to perform some action. Depending on the exact timing, one or other goes first. If one of the threads goes first, everything works correctly, but if another one goes first, an error, possibly fatal, occurs.

There is considerable material on this subject in my class notes for OS. We will just use this one example.

A1: LOAD  r1,x     B1: LOAD  r2,x
A2: ADD   r1,1     B2: SUB   r2,1
A3: STORE r1,x     B3: STORE r2,x

Imagine two threads both access the shared variable x, which is initially 10. One process executes x:=x+1; the other x:=x-1. Hence, at the conclusion, x should again be 10. However increment and decrement are not atomic; each becomes 3 instructions as shown on the right. As a result x can actually end up equal to 9, 10, or 11.

12.1.1: The Case for Multithreaded Programs

12.1.2: (Multiprocessor) Architectures

We consider what computer architectures support concurrency.

  1. Multiprocessor computer systems. Once rare, these are now common and provide a natural match for concurrency. The simplest possibility to think about is to have one CPU for each thread. Normally, the hardware permits each processor to access all the memory with simple Loads and Stores (when this is not possible the system is normally called a multicomputer).
  2. Pseudo-parallelism on a uniprocessor. Another possibility is just a boring single processor sequential machine running a plain vanilla operating system (e.g., Unix/Linux, Windows, or MAC OS). Just as two or more users' jobs can be run together, with the OS switching between them; two or more threads from the same job can be run together the same way. This is sometimes called pseudo-parallelism; or quasiparallelism real parallelism being reserved for the case where there are multiple processors. Most concurrency issues apply to both pseudo- and real parallelism.
  3. N threads running on M<N CPUs. A mixture of the above two cases. The threads are scheduled across all the processor.
  4. As we learn when studying operating systems, various devices on a computer system (e.g., a DMA disk controller) have their own CPUs and act concurrently with the main CPU. This is not exposed to programing languages and is not studied in this course.
  5. Clusters. Multiple computers (some multiprocessors) physically near each other connected on a high speed local network. Each computer system is independent.
  6. Multiple Computers on a network. This is typically called distributed computing. The communication cost is very high and, as a result, the languages used are rather different. We do not study them.

It is difficult to have a large number of processors all share the same memory (unless you are willing for some of the processors to have slow access to some memory). As a result, supercomputers with many thousands of processors do not have memory efficiently shared by all the processors, so a shared memory model would need be implemented in terms of message passing.

A serious hardware issue is to keep the caches of the individual CPUs consistent (the terminology is coherent, but the right word is consistent).

Homework: CYU 2, 4, 7.

12.2: Concurrent Programming Fundamentals

12.2.1: Communication and Synchronization

As we mentioned at the beginning, communication and synchronization are two important challenges for any concurrent programming model.

If multiple threads are to cooperate on solving a single problem, they must be able to communicate information and results from one to the other. The two ways to do this are via shared memory and message passing.

Shared Memory With the shared memory model some of the program's variables are accessible to multiple threads. Any pair of threads that have access to the same variable, can use it to communicate. Either thread can write to the variable and the other thread can then read the value written (why is then colored red?).

With shared memory, thread synchronization is typically explicit. Without some extra control, the second thread might read the shared variable before the first thread has written it.

Message Passing In a message passing model, the threads have no common state. For information to flow from thread A to thread B, A must perform an explicit send and B must perform a receive. This action of explicitly sending and receiving, also synchronizes the two threads; the receive in B will wait (perhaps forever!) for the send in A to occur. Indeed sometimes a send/receive is just used for synchronization; no useful data is transmitted.

12.2.A: Common Synchronization Idioms

We look at three common forms of synchronization: mutual exclusion, condition synchronization, and barriers.

Mutual Exclusion

Recall the race condition we discussed earlier where incrementing and decrementing a variable gave unpredictable results. The cause was that we viewed x:=x+1 as an atomic (i.e., indivisible) operation; whereas, the implementation was three atomic units and, with bad luck, thread switching occurred during the three-instruction sequence.

So we need to enforce the condition that while one thread executes x:=x+1 the other cannot execute x:=x-1 That is, executing either x:=x+1 or x:=x-1 excludes executing the other (until the former finishes).

This was a low level example involving shared memory.

Higher level mutual exclusions also occur. For example, if one thread is printing or sending audio to a speaker, another thread cannot use the device until the first is finished. (OS students know that sometimes spooling can be used to soften this requirement, but we will not discuss that here.)

Condition Synchronization

A thread is blocked until a specific condition is met. Consider the example where we had two threads:

  1. This thread is searching a database look for items that match a pre-specified pattern (Google-like).
  2. This thread examines all the hits from thread A, ranks them, and displays the results on the screen.
When thread B finishes displaying all the hits to date, it must wait for the condition thread A finds another hit.


Very roughly speaking, programs that predict the weather proceed as follows.

  1. The earth's surface is divided by longitude and latitude into many (say 100,000) areas. The atmosphere is divided into several (say 10) layers based on altitude. Thus we have a very large number (say 1,000,000) of 3D regions or cells. Initially, we know the current conditions (e.g., pressure, humidity, temperature, etc) at each cell (this is not true in practice, but assume it is).
  2. There are mathematical models that predict quite accurately what the conditions will be in a cell a short time (say a minute) from now based on current conditions at the cell and at the neighboring cells .
  3. We have many (say 100) threads working on the problem and assign each a bunch (10,000) regions. Each thread computes the new conditions at each of its regions.
  4. When all threads have finished the computation, they can all proceed to advance time another minute.
  5. The rough beginnings of an Ada solution along these lines is here.

The key synchronization requirement is that all the threads must be working on the same time step and thus at the end of each time step. Each thread must wait for all the other threads to catch up.

The act of waiting for all threads to reach the same point is called barrier synchronization. I guess you think of the point as a barrier none can cross until they all arrive to help.

12.2.2: Languages and Libraries

The language support for concurrency varies. The methods used for thread creation are in the next section; here we just give a brief overview of the support in different languages.

12.2.3: Thread Creation Syntax


forall (i=1:n-1) A(i) = B(i)+C(i) A(1+1) = A(i)+A(i+1) end forall

There is a wide variety of syntax (and semantics) employed for thread creation.

Homework: CYU 12, 16, 19.

12.2.4: Implementation of Threads

12.3: Implementing Synchronization

12.3.1: Busy-Wait Synchronization

12.3.2: Nonblocking Algorithms

12.3.3: Memory Consistency Models

12.3.4: Scheduler Implementation

12.3.5: Semaphores

12.4: Language-Level Mechanisms

12.4.1: Monitors

12.4.2: Conditional Critical Regions

12.4.3: Synchronization in Java

12.4.4: Transactional Memory

12.4.5: Implicit Synchronization

12.5: Message Passing

12.A: Concurrency and Tasks in Ada

Ada achieves concurrency by means of tasks. The language supplies both tasks, used when each task is different, and task types, used when you want more than one instance of the same task or need the task to be part of a larger structure. We can also specify heap allocation, by defining an access to a task type (access is Ada-speak for pointer to) and then issue the statement new.

12.A.1: Defining Ada Tasks

A Simple Task

with Ada.Text_IO; use Ada.Text_IO;
procedure Adatask is
   task T;
   task body T is
      Put_Line("Hello from T");
   end T;
   Put_Line("Hello from Adatask");
end Adatask;

with Ada.Text_IO; use Ada.Text_IO; procedure Adatask is task type TT; type TTA10T is array (1..10) of TT; task body TT is begin Put_Line("Hello from TT"); end TT; TTA10: TTA10T; begin Put_Line("Hello from Adatask"); end Adatask;
with Ada.Text_IO; use Ada.Text_IO; procedure Adatask is task type TT; type TTP is access TT; task body TT is begin Put_Line("Hello from TT"); end TT; TT3, TT4: TTP; begin TT3 := new TT; TT4 := new TT; Put_Line("Hello from Adatask"); end Adatask;

The first frame on the right shows a trivial task T, declared inside a trivial procedure Adatask. The output shows both hello's, as expected.

Although trivial, this example does bring up three points.

  1. Which print appears first in the output?
    Answer: Not determined by Ada.
  2. Is there just one task (T) or there two? If only one, why is this called concurrent? If two, what is the other task?
    Answer: There are two. The other task is the main task. That is, officially all the Ada programs we have seen before have one task.
  3. When do the tasks start and finish?
    Answer: The main task starts when the program begins. Task T starts at the next begin. This is important since all the declarations of Adatask are visible to T and we cannot be sure they have been created until the declarations of Adatask have finished elaboration, which occurs at the next begin.

Task Types

As mentioned, in addition to defining a single task, Ada permits us to define a task type and then declare individual tasks of that type. The second frame illustrates this possibility, again with a trivial example.

The only difference between the task declaration in frame one and the task type declaration in frame two is the addition of the single word type. We also declared a new type TTA10T, a size 10 array of the task type TT, and finally declared TTA10, one of these arrays (there is syntax to do all this in one line).

All 10 tasks in the array begin execution at the next begin.

There are eleven lines of output: one hello from Adatask and one from each member of the array. The order in which these lines are printed is again not specified by the language.

Dynamically Allocated Tasks

The third frame shows dynamic allocation of tasks. We define the type TTP as an access (pointer) to the task type TT and define two such access variables. As usual with pointer types, we need to execute new to create the tasks themselves.

The dynamically allocated task can begin execution as soon as the new operation completes.

As expected we get three hello's, one from Adatask and two from TT, in an unspecified order.


The first two examples showed statically allocated tasks; the third example was dynamically allocated. In all three cases the task terminates when it reaches end TT;.

As with statically allocated data, the statically allocated tasks cease to exist when they go out of scope. In the examples, that occurs at end Adatask. However, unlike the situation with data, if the main task reaches end Adatask before all the tasks have terminated, the main task waits. That is the end Adatask acts as a barrier.

For the dynamically allocated task, the rules are a little different. The barrier is the end of the unit in which the access type is defined. For the example given, it is the same end as with the statically allocated tasks, but in more complicated examples, the two ends are different.

These remarks about the main taks also apply if a created task itself creates another task.

12.A.2: Task Communication (A Producer/Consumer/Buffer Example)

On the right we see the code for a trivial version of a very important example, the producer/consumer problem also called the bounded buffer problem. The code includes 5 tasks (it could have been 4): the main task, two producer tasks Prod1 and Prod2, a consumer task Con, and a bounded buffer task Buffer. I have color coded the tasks to aid in distinguishing them visually, but please note that these are not separate frames, i.e., the entire code sequence is in one file buf.adb and compiles and runs by itself.

with Ada.Text_IO;
with Ada.Integer_Text_IO;
procedure Buf is
   subtype Item is Integer;
task Buffer is entry Put(X: in Item); entry Get(X: out Item); end Buffer; task body Buffer is V: Item; begin loop accept Put(X: in Item) do V:=X; end Put; accept Get(X: out Item) do X:=V; end Get; end loop; end Buffer;
task type Prod is entry Start (Init: in Item); end Prod; task body Prod is First: Item; begin accept Start (Init: in Item) do First := Init; end Start; for I in First+1 .. First+100 loop Buffer.Put(I); end loop; end Prod; Prod1, Prod2 : Prod;
task Con; task body Con is X: Item; begin for I in 1 .. 200 loop Buffer.Get(X); Ada.Integer_Text_IO.Put(X); Ada.Text_IO.New_LIne; end loop; end Con;
begin Prod1.Start(100); Prod2.Start(500); end Buf;

Task Services

A task can perform services for another task. This ability must be declared in the specification with entry statements. We see two entry's in the Buffer task and one in each producer. As we have seen before tasks can also perform actions on their own without responding to requests from other tasks.

Synchronization via the Rendezvous

Ada has two methods for synchronizing tasks: shared variables and message passing. Using the scoping rules we learned early in the course, we see that all the colored tasks could access any variables declared in Buf. In this particular example there are no such variables and message passing is used.

The specific mechanism is called rendezvous and works as follows.

How Does the Example Work?

If you compile and run the code with

    gnatmake buf.adb; ./buf
the main task, namely procedure Buf begins execution.

All the tasks declared in Buf are statically allocated so they begin execution when Buf reaches the begin. Now five tasks can execute Buf, Con, Prod1, Prod2, and Buffer. Which one executes?

The answer is important: We don't know.. So we must consider all cases.

Now it gets complicated as there are several possibilities. We will discuss it in class.

with Ada.Text_IO;
with Ada.Integer_Text_IO;
procedure MortalBuf is
   subtype Item is Integer;
task Buffer is entry Put(X: in Item); entry Get(X: out Item); end Buffer; task body Buffer is V: Item; begin loop select accept Put(X: in Item) do V:=X; end Put; or Terminate; end select; accept Get(X: out Item) do X:=V; end Get; end loop; end Buffer;
task type Prod is entry Start (Init: in Item); end Prod; task body Prod is First: Item; begin accept Start (Init: in Item) do First := Init; end Start; for I in First+1 .. First+100 loop Buffer.Put(I); end loop; end Prod; Prod1, Prod2 : Prod;
task Con; task body Con is X: Item; begin for I in 1 .. 200 loop Buffer.Get(X); Ada.Integer_Text_IO.Put(X); Ada.Text_IO.New_LIne; end loop; end Con;
begin Prod1.Start(100); Prod2.Start(500); end MortalBuf;

Making the Buffer Task Mortal (select and terminate)

You may have noticed that the Buffer task is an infinite loop and hence never dies. As a result the main task (procedure Buf) waits at its end statement forever.

It is not unreasonable for servers like Buffer to be infinite loops waiting for possible client requests. However, in this case, all possible clients cannot send any future requests: The two Prod's and Con quickly complete execution and terminate when the main task reaches end.

The main task, although reaching its end, will not terminate since one of the tasks it declared (namely Buffer is still active). Thus two of the five tasks do not terminate despite there being no chance of either doing anything.

The code on the right shows one possible solutions to this annoyance. The only change is to the body of Buffer.

The select/or Statement: In its basic form the select statement is used when several entry calls are possible at this point in the execution and the server wishes to pick on arbitrarily. In that case you would see

    select accept or accept or ... or accept end select
However, there are other possibilities besides for accept.

The code on the right uses terminate, which ends the task when it can be determined that no further progress can occur. As written, we permit termination only when the buffer is empty (i.e., only when the buffer is waiting for a Put entry call.

with Ada.Integer_Text_IO;
Procedure MutexTest is
   X : Integer := 10;
   declare -- inner block
task type MutexType is entry Lock; entry Unlock; end MutexType; task body MutexType is begin loop accept Lock; accept Unlock; end loop; end MutexType; Mutex: MutexType;
task Adder; task body Adder is begin Mutex.Lock; X := X+1; Mutex.Unlock; end Adder;
task Subtracter; task body Subtracter is begin Mutex.Lock; X := X-1; Mutex.Unlock; end;
begin -- activates 3 tasks null; end; -- wait for all done Ada.Integer_Text_IO.Put(X); end MutexTest;

12.A.3: Mutual Exclusion, Semaphores, and Adding and Subtracting One

Recall that, early in the chapter we discussed race conditions and showed how, starting with X=10 and then executing X:=X+1 and X:=X-1, we might not get X=10 as the final result. The problem was that neither X:=X+1 nor X:=X-1 is atomic and hence if the two statements are executed by different threads, their subparts might be interleaved.

We mentioned that what was needed was for execution of X:=X+1 to exclude the execution of X:=X-1 and vice versa. That is, the executions of X:=X+1 and X:=X-1 should be mutually exclusive.

The code on the right solves this problem with a server task called Mutex (mutual exclusion) that accepts alternating entry calls of lock and unlock. Since the accepts must alternate, two locks must be separated by an unlock.

Thus if sections of code are surrounded by lock/unlock pairs, execution cannot enter the second until the first exits.

As in the previous example, this server does not terminate. Termination can be achieved using the select and terminate statements as we did before.

accept's Without Bodies

Note that the accept statements in the Mutex have no body. Hence they transmit no information from client to server or vice versa. Nonetheless, they are extremely useful as they enforce some synchronization. That is, they limit the possible interleavings of the tasks. If a client reaches the entry call before the server reaches the accept, the client waits. If the server arrives first, it waits.


Structures used to enforce mutual exclusion are also called semaphores. The lock is called P and the unlock V. These are the first letters in Dutch for words chosen by Dijkstra, the discoverer of semaphores.

Semaphore definitions are usually a little different. For example, I believe two consecutive V's would have the same effect as just one. The code on the right would block the second of two consecutive unlock entry calls.

P/V are sometimes called down/up.

Delays and Time

delay 4.3;
delay until t;

The Ada delay statement causes the task executing it to become quiescent for a specified period or until a specified time.

12.A.4: Conditional Communication

So far our accept statements were unconditional. Unless, it was impossible for an entry call to occur, the accept would wait as long as necessary. We will now see how one can set requirements (conditions) on accepting an entry call and also on limiting the amount of time we are willing to wait.

  accept Entry1(...) do
  accept Entry2(...) do
  delay 2*Minutes+30*Seconds;
  -- action on timeout
end select;

select Server1.Entry1(...); or delay 1*Minute; -- action on timeout end select;
select Server1.Entry1(...); else -- action on timeout end select;

Timed Entry Calls

The branches of our select statements have normally begun with an accept. The one exception has been a terminate, used to detect when no entry calls are logically possible.

In the first frame on the right we see another example, a time-out. If, after the server arrives at the select, no entry1 or entry2 call is received within 2 minutes and 30 seconds (Minutes and Seconds are built in constants of type Duration) then the third arm is selected and the written timeout actions are performed.

Clients can also set time-outs as shown in the second frame.

Finally, we note that the really impatient who will only make an entry call if the server is currently waiting can use a delay of zero. An alternative is to else clause as the client did in the third frame. This form is available for servers as well.

    when QNotFull => 
      accept Insert(X: in Integer) do
    when QNotEmpty => 
      accept Remove(X: out Integer) do
  end select;
end loop;

Conditional Acceptance

A server can refuse to accept an entry call if the state is unfavorable. For example a sever thread implementing a queue, might have code something like shown on the right.

If the queue is full, only a removal is permitted; if the queue is empty, only an insertion is permitted; if the queue is neither full nor empty, then either operation is permitted and if both are pending when the select is executed a non-deterministic choice is made.

procedure Barrier is
  NTimeSteps: Positive := 50;
  NThreads: Positive := 20;
  NCells: Positive := 10000;
  CellsPerThread: Positive := NCells/NThreads;
  type CellType is record
    X,Y,Z: Float := 0.0;
  end record;
  type CellArray is array (0..NCells-1) of CellType;
  OldCell,NewCell: CellArray;
function Compute(Data: in CellArray; CellNum: in Natural) return Celltype is begin return (1.0, 2.0, 3.0); -- weather science goes here end Compute;
task Controller is entry Finish; entry Continue; end Controller; task body Controller is begin loop for I in 1..NThreads loop accept Finish; end loop; for I in 1..NThreads loop accept Continue; end loop; end loop; end Controller;
task type WorkerType is entry Start(MyNum: in Natural); end WorkerType; task body WorkerType is MyFirstCell: Natural; -- where I start begin accept Start(MyNum: in natural) do MyFirstCell := MyNum*CellsPerThread; end; for T in 1..NTimeSteps/2 loop for I in MyFirstCell .. MyFirstCell+CellsPerThread-1 loop NewCell(I) := Compute(OldCell,I); end loop; Controller.Finish; Controller.Continue; for I in MyFirstCell .. MyFirstCell+CellsPerThread-1 loop OldCell(I) := Compute(NewCell,I); end loop; Controller.Finish; Controller.Continue; end loop; end WorkerType; Worker: array (1..NThreads) of WorkerType;
begin for I in 0..NThreads-1 loop Worker(I).Start(I); -- tell worker its number end loop; end Barrier;

12.A.5: Barrier Synchronization

We described the important problem of barrier synchronization before and illustrated its use with a very high level description of weather prediction.

The code on the right uses barriers to synchronize a bunch of workers solving the weather code. Recall that the basic idea is that, using rather fancy meteorological science and mathematics, we can compute the weather (really it isn't the weather, but certain primary variables like pressure) at a small region or cell a short time in the future knowing the current weather at the cell and its neighbors. Naturally, I haven't reproduce that science here. Instead, there is a (red) function compute that, given the current weather everywhere, magically computes the new weather at a specified cell.

There are NCells and NThreads called workers each of which computes NCellsPerThread cells.

Recall that all the cells are being updated at once and to compute the updated value at a cell we need the original value at a bunch of cells. We use a standard technique to solve this problem and have two arrays of values at every cells. In the odd iterations we compute newcells from oldcellts. Then there is a barrier, we compute switch roles, and compute oldcells from newcells.

Look carefully at the controller task and how it is used to achieve the barrier.

The main thread starts all the workers, giving each its number so that it can compute its range of cells.

12.A.6: Shared Memory, Protected Objects, and Readers/Writers

package Scary is
   type Pair is
         A: Integer;
         B: Integer;
      end record;
   P: Pair := (0,0);
   function ReadPair Return Pair;
   Procedure WritePair (X: in Pair);
end Scary;

package body Scary is function ReadPair Return Pair is begin return P; end ReadPair; Procedure WritePair (X: in Pair) is begin P.A := X.A; P.B := X.B; end WritePair; begin null; end Scary;
with Ada.Text_IO; use Ada.Text_IO; with Ada.Integer_Text_IO; use Ada.Integer_Text_IO; with Scary; Procedure rw is
task Update; task body Update is P: Scary.Pair; begin for I in 1 .. 1_000_000_000 loop P := Scary.ReadPair; P.A := P.A+1; P.B := P.B+1; Scary.WritePair(P); end loop; end Update;
task Check; task body Check is P: Scary.Pair; begin for I in 1 .. 1_000_000_000 loop P := Scary.ReadPair; if P.A /= P.B then Put_Line("Error!" ); abort Update, Check; end if; end loop; end Check;
begin null; end rw;

As we have remarked previously, the normal scope rules give us shared variables. But, as we have seen race conditions can occur and even adding and subtracting 1 are not guaranteed two work if multiple threads are active. On the right is scary code that should not fail, but it does.

The update increments each variable by one so X and Y should be the same. However, occasionally Check finds them unequal, because Update is in the middle of an iteration. Note that there is no danger in having Scary.ReadPair executing multiple times concurrently since it does not change any state. However, when Scary.WritePair is active, an error can occur if some other Scary.WritePair or some Scary.ReadPair is active.

This is the well-known Readers/Writers problem.

We could protect both Scary.WritePair and Scary.ReadPair with semaphores thereby ensuring mutual exclusion. This, however, does too much! It forbids concurrent execution of Scary.ReadPair.

Ada has a solution to readers/writers built into the language in the form of protected objects. Procedures are treated as writers and demand exclusive access; functions are readers and can run concurrently.

package SafePack is
   type Pair is
	 A: Integer;
	 B: Integer;
      end record;
   P: Pair := (0,0);
   protected Safe is
      function ReadPair Return Pair;
      Procedure WritePair (X: in Pair);
   end Safe;
end SafePack;

package body SafePack is protected body Safe is function ReadPair Return Pair is begin return P; end ReadPair; Procedure WritePair (X: in Pair) is begin P.A := X.A; P.B := X.B; end WritePair; end Safe; begin null; end SafePack;
with Ada.Text_IO; use Ada.Text_IO; with Ada.Integer_Text_IO; use Ada.Integer_Text_IO; with SafePack; Procedure SafeRW is
task Update; task body Update is P: SafePack.Pair; begin for I in 1 .. 1_000_000_000 loop P := SafePack.Safe.ReadPair; P.A := P.A+1; P.B := P.B+1; SafePack.Safe.WritePair(P); end loop; end Update;
task Check; task body Check is P: SafePack.Pair; begin for I in 1 .. 1_000_000_000 loop P := SafePack.Safe.ReadPair; if P.A /= P.B then Put_Line("Error!" ); abort Update, Check; end if; end loop; end Check;
begin null; end SafeRW;

Homework: When are tasks in Ada started? When do they terminate?

Homework: CYU 29. This can also be written as: What is the difference between readers/writers synchronization and mutual exclusion?.

Homework: CYU 30

Homework: Write an Ada program with two tasks that behaves as follows. The tasks share a variable N that is initialized to a positive integer (say 30). One task computes the sum 12+22+...+N2+(N+1)2. The other computes the sum (1+2+...+N)2. The tasks rendezvous and exchange their sums. After the rendezvous the task that had the larger sum, prints out both values. If the values are equal the first task prints.

12.B: Concurrency and Threads in Java

Java has two ways for the user to implement threads.

  1. Extend the class thread.
  2. Implement the interface runnable.

public class ExThread {
  public static class MyThread extends Thread {
    public void run() {
      System.out.println ("A new thread!");
  public static void main (String[] args) {
    MyThread T = new MyThread();

12.B.3: Extending the Class thread

Java has a built in class Thread (specifically it is java.lang.Thread) which can be extended just like other classes. However creating an object of this class, brings a new thread of control to life. This thread will begin executing as soon as its start member is called. The start member is built-in and calls the method run, which the user writes.

A trivial example is shown on the right. I simply create an object T of class MyThread and called its start method, which in turn called its run method, which I have overridden.

public class Play {
  public static class PingPong extends Thread {
    private String word;
    private int delay;
    PingPong (String whatToSay, int delayTime) {
      delay= delayTime;
    public void run() {
      try {
        for (;;) {
        System.out.print(word + " ");
      } catch (InterruptedException e) {
  public static void main (String[] args) {
    PingPong T1 = new PingPong("Ping", 35);
    PingPong T2 = new PingPong("Pong", 100);

A Fun Example (from Osinski)

Now we start two threads, which will play ping pong. One thread says ping and the other pong. However, the one saying ping must be younger and only needs to rest 35ms after speaking; whereas the slower partner needs to rest 100. As a result we get many more pings than pongs.

A sleep method call (in java.lang.Thread) can throw an InterruptedException. My run cannot throw this exception since the run it is overridding (in java.lang.Thread) does not. Hence, I must catch it.

Here is some sample output from a run of this cutie. It would run forever so I hit control-C.

Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping Pong Ping Ping Ping

public class ImpRun {
  public static class MyThread implements Runnable {
    public void run() {
      System.out.println ("A new thread!");
  public static void main (String[] args) {
    MyThread MT = new MyThread();
    Thread T = new Thread(MT);

12.B.4: Implementing the interface Runnable

The code on the right is the analogue of the first example above, but achieved via implementing Runnable rather than extending Thread.

Extending Thread vs. Implementing Runnable

For the trivial example shown, the two techniques look to be essentially the same. However, there are differences.

12.B.5: Synchronization in Java

public class SafeAccount {
    private int currentBalance = 0;
    public synchronized void deposit(int d) {
        currentBalance += d;
    public synchronized void withdraw(int w) {
        currentBalance -= w;
    public synchronized int balance() {
        return currentBalance;
SafeAccount acct1, acct2;

Synchronized Methods

Methods declared to be synchronized are mutually exclusive (over a given object). For example, the code on the right insures that all accesses to acct1 will be mutually exclusive. In fact, this is more synchronization than we need since concurrent balance inquiries could be allowed.

Similarly, all access to acct2 are mutually exclusive. However one access to acct1 can be concurrent with one access to acct2.

Wait/Notify (Message Passing)

Java also has an analogue of the Ada rendezvous, which is based on the Wait/Notify constructs. Like rendezvous, this technique primarily uses message passing.

Homework: Look at my Scary and SafePack programs (they are bad and good attempts to implement readers/writers. Write something roughly equivalent in Java, using synchronized methods to make the bad good, similarly to the way I used protected to make my bad good.

The End: Good luck on the final