A performance study of general-purpose applications on graphics processors using CUDA
A performance study of general-purpose applications on graphics processors using CUDA
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DOI：
10.1016/j.jpdc.2008.05.014
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发表时间：
2008-10-01
影响因子：
3.8
通讯作者：
Skadron, Kevin
中科院分区：
文献类型：
--
作者：
Che, Shuai;Boyer, Michael;Skadron, Kevin
图形处理器（GPU）提供大量简单、数据并行、深度多线程的内核和高内存带宽。GPU架构正变得越来越可编程，与当代通用处理器（CPU）相比，为各种通用应用提供了巨大的加速潜力。本文使用NVIDIA的类C CUDA语言和他们最近推出的GTX 260 GPU的工程示例来探索GPU对各种应用类型的有效性，并描述了一些特定的编码习惯，以提高其在GPU上的性能。GPU性能与单核和多核CPU性能进行了比较，多核CPU实现使用OpenMP编写。本文还讨论了CUDA编程模型的优点和效率低下之处，以及一些可能更易于使用并更容易支持更大规模应用程序的理想功能。(c)2008年爱思唯尔公司All rights reserved.
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.