DEPARTMENT OF COMPUTER SCIENCE
DOCTORAL DISSERTATION DEFENSE


Candidate: Naftali Schwartz
Advisor: Zvi Kedem

Automatic Parallelization:
An Incremental, Optimistic, Practical Approach

10:00 a.m., Friday, June 18, 1999
7th floor conference room, 719 Broadway




Abstract

The historic focus of Automatic Parallelization efforts has been limited in two ways. First, parallelization has generally been attempted only on codes which can be proven to be parallelizeable. Unfortunately, the requisite dependence analysis is undecidable, and today's applications demonstrate that this restriction is more than theoretical. Second, parallel program generation has generally been geared to custom multiprocessing hardware. Although a network of commodity workstations (NOW) could theoretically be harnessed to serve as a multiprocessing platform, the NOW has characteristics which are at odds with effective utilization.

This thesis shows that by restricting our attention to the important domain of ``embarrassingly parallel'' applications, leveraging existing scalable and efficient network services, and carefully orchestrating a synergy between compile-time transformations and a small runtime system, we can achieve a parallelization that not only works in the face of inconclusive program analysis, but is indeed efficient for the NOW. We optimistically parallelize loops whose memory access behavior is unknown, relying on the runtime system to provide efficient detection and recovery in the case of an overly optimistic transformation. Unlike previous work in speculative parallelization, we provide a methodology which is not tied to the Fortran language, making it feasible as a generally useful approach. Our runtime system implements Two-Phase Idempotent Eager Scheduling (TIES) for efficient network execution, providing an Automatic Parallelization platform with performance scalability for the NOW.

Our transformation divides the original program into a server and zero or more clients. The server program is a specialization of the original application with each parallel loop replaced with a scheduling call to the client which comprises the body of that parallel loop. The scheduler remotely executes the appropriate instances of this client on available machines.

We describe the transformation and runtime system in detail, and report on the automatic transformation achieved by our implementation prototype in two case studies. In each of these cases, we were able to automatically locate the important coarse-grained loops, construct a shared-memory layout, and generate appropriate server and client code. Furthermore, we show that our generated parallel programs achieve near-linear speedups for sufficiently large problem sizes.