A performance study of general-purpose applications on graphics processors using CUDA

被引：345

作者：

Che, Shuai ^{[1
]}

Boyer, Michael ^{[1
]}

Meng, Jiayuan ^{[1
]}

Tarjan, David ^{[1
]}

Sheaffer, Jeremy W. ^{[1
]}

Skadron, Kevin ^{[1
]}

机构：

[1] Univ Virginia, Dept Comp Sci, Charlottesville, VA 22904 USA

来源：

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING | 2008年 / 68卷 / 10期

基金：

美国国家科学基金会;

关键词：

Graphics processors; GPU; GPGPU; CUDA; OpenMP; Parallel programming; Heterogeneous computing organizations; Multicore; Manycore;

D O I：

10.1016/j.jpdc.2008.05.014

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 general-purpose 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. (c) 2008 Elsevier Inc. All rights reserved.

引用

页码：1370 / 1380

页数：11

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