异构并行系统的协同执行环境

A. Ilic, L. Sousa
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引用次数: 4

摘要

如今,商用计算机是复杂的异构系统,提供了巨大的计算能力。然而,为了利用这种能力,我们必须协调使用具有不同特性的处理单元。这种分布式内存系统使用相对较慢的互连网络,例如系统总线。因此,大多数时候我们只单独利用中央处理单元(CPU)或处理加速器,它们是更简单的同构子系统。在本文中,我们提出了一个在异构系统中利用数据并行性的协作执行环境。结果表明,该环境可用于对CPU和图形处理器(gpu)进行编程,以协同计算矩阵乘法和快速傅里叶变换(FFT)。实验结果表明,当CPU和GPU同时使用时,可以获得显著的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative execution environment for heterogeneous parallel systems
Nowadays, commodity computers are complex heterogeneous systems that provide a huge amount of computational power. However, to take advantage of this power we have to orchestrate the use of processing units with different characteristics. Such distributed memory systems make use of relatively slow interconnection networks, such as system buses. Therefore, most of the time we only individually take advantage of the central processing unit (CPU) or processing accelerators, which are simpler homogeneous subsystems. In this paper we propose a collaborative execution environment for exploiting data parallelism in a heterogeneous system. It is shown that this environment can be applied to program both CPU and graphics processing units (GPUs) to collaboratively compute matrix multiplication and fast Fourier transform (FFT). Experimental results show that significant performance benefits are achieved when both CPU and GPU are used.
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