Processing Units (GPUs): Architecture and Programming
Welcome students! ... to
the Graphics Processing Units course, edition Fall 2014.
I will keep updating this page regularly. If you have
questions related to this course feel free to email me.
Here is some basic information:
course examines the architecture and capabilities of
modern GPUs (graphics processing unit).
Many computations can be performed
faster on the GPU than on a traditional CPU.
This is why GPUs exist now in almost all
computers (from tablets to supercomputers);
majority of Top 500 supercomputers in the world are built
GPUs are now used for a diverse set of applications not only
traditional graphics applications.
This introduces the concept of
general-purpose GPUs or GPGPUs.
In this course, we will cover architectural aspects of modern GPUs.
We will also learn how to program GPUs to solve different type of
problems and how to make
the best use of its hardware.
- Some other suggested, but not required, books:
Final exam: Dec 17, 7:10-9pm Room 317
- Our grader: Jiakai Zhang zhjk (AT) nyu dot edu
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list for the course, if you have not done it already. You can manage your subscription by clicking here.
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Assignment 1: Due Sep 17th, 2014 - solutions - comments
To setup you machine to work with out CUDA cluster:
First, login to your CIMS account
Once logged in, ssh to cuda1
Now, you can get setup running CUDA code by following these instructions:
cp -r /usr/local/pkg/cuda
Suggested projects (but feel free to suggest your own):
Links (Geeky stuff about GPUs)
C programming guide
digital 3D rendered film (Thanks William Ward)
with Ed Catmull (Thanks William Ward)
GPU computing seminars
of CUDA articles at Dr. Dobb's
2.0 reference card
GPU accelerated machine learning (Thanks Darshan Hegde for the link)
Simulators and Tools:
(simulates both GPUs and multicore)
(dynamic compilation for PTX)