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A Performance Study of General-Purpose Applications on Graphics Processors Using CUDA
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BibTeX
@MISC{Che_aperformance,
author = {Shuai Che and Michael Boyer and Jiayuan Meng and David Tarjan and Jeremy W. Sheaffer and Kevin Skadron},
title = {A Performance Study of General-Purpose Applications on Graphics Processors Using CUDA},
year = {}
}
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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 specific 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 inefficiencies 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.
Keyphrases
graphic processor using cuda general-purpose application performance study dramatic speedup high memory bandwidth nvidia c-like cuda language vast number gpu architecture engineering sample gpu performance contemporary general-purpose processor multithreaded core graphic processor generalpurpose application application type desirable feature cuda programming model specific coding idiom multicore cpu implementation multicore cpu performance
