GPU并行计算分析

2009 DoD High Performance Computing Modernization Program Users Group Conference Pub Date : 2009-06-15 DOI:10.1109/HPCMP-UGC.2009.59

S. Park

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引用次数: 6

摘要

并行系统在计算世界中变得无处不在，多核处理器、异构Cell宽带引擎和高度并行的图形处理单元(gpu)就是证明。所有并行系统都有一个共同的要求，即并行编程是利用多核所必需的。由于这种趋势，多核cpu不再是一个明显的赢家，因为它的时钟频率达到峰值，并且涉及到为多核架构并行化代码的编程工作。考虑到这些缺点，数据并行应用程序可能会从GPU辅助计算中受益。gpu是最流行和最便宜的加速器。为了评估基于gpu的计算，选择了一种用于雷达成像应用的浮点密集算法进行分析。本文试图对CPU和GPU实现进行公平的性能比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An Analysis of GPU Parallel Computing

Parallel systems are becoming ubiquitous in the world of computing as evidenced by multi-core processors, heterogeneous Cell broadband engine, and highly parallel graphics processing units (GPUs). All parallel systems share a requirement that parallel programming is necessary to leverage multiple cores. As a result of this trend, multi-core CPUs are no longer a clear winner due to its peaked clock frequency and programming effort involved in parallelizing code for multi-core architecture. Given such drawbacks, dataparallel applications might benefit from GPU assisted computing. GPUs are the most popular and inexpensive accelerators. To evaluate GPU-based computing, a floating-point intensive algorithm for a radar imaging application is chosen for analysis. The paper attempts to present a fair performance comparison of CPU and GPU implementations.

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2009 DoD High Performance Computing Modernization Program Users Group Conference

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