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"A Performance Study of General-Purpose Applications on Graphics Processors Using CUDA"
Shuai Che, Michael Boyer, Jiayuan Meng, David Tarjan, Jeremy W. Sheaffer, Kevin Skadron, in "Journal of Parallel and Distributed Computing", October 2008 |
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| Author(s): |
Shuai Che, Michael Boyer, Jiayuan Meng, David Tarjan, Jeremy W. Sheaffer, Kevin Skadron |
| Date: |
October 2008 |
| URL: |
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| Abstract: |
Graphics processors (GPUs) provide a vast number of simple, data-parallel, deeply multithreaded
cores and high memory bandwidths. GPU architectures are becoming increasingly
programmable, offering the potential for dramatic speedups for a variety of generalpurpose
applications compared to contemporary general-purpose processors (CPUs). This
paper uses NVIDIA's C-like CUDA language and an engineering sample of their recently
introduced GTX 260 GPU to explore the effectiveness of GPUs for a variety of application
types, and describes some specic coding idioms that improve their performance on the
GPU. GPU performance is compared to both single-core and multicore CPU performance,
with multicore CPU implementations written using OpenMP. The paper also discusses advantages
and inefciencies of the CUDA programming model and some desirable features
that might allow for greater ease of use and also more readily support a larger body of
applications. |
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