A tuning framework for software-managed memory hierarchies

Manman Ren, Ji Young Park, M. Houston, A. Aiken, W. Dally
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引用次数: 43

Abstract

Achieving good performance on a modern machine with a multi-level memory hierarchy, and in particular on a machine with software-managed memories, requires precise tuning of programs to the machine's particular characteristics. A large program on a multi-level machine can easily expose tens or hundreds of inter-dependent parameters which require tuning, and manually searching the resultant large, non-linear space of program parameters is a tedious process of trial-and-error. In this paper we present a general framework for automatically tuning general applications to machines with software-managed memory hierarchies. We evaluate our framework by measuring the performance of benchmarks that are tuned for a range of machines with different memory hierarchy configurations: a cluster of Intel P4 Xeon processors, a single Cell processor, and a cluster of Sony Playstation3's.
软件管理内存层次结构的调优框架
在具有多级内存层次结构的现代机器上,特别是在具有软件管理内存的机器上,要获得良好的性能,需要根据机器的特定特性对程序进行精确的调优。多级机器上的大型程序很容易暴露出数十或数百个需要调整的相互依赖的参数,手动搜索由此产生的大型非线性程序参数空间是一个乏味的试错过程。在本文中,我们提出了一个通用框架,用于自动调整通用应用程序到具有软件管理内存层次结构的机器。我们通过测量针对一系列具有不同内存层次配置的机器进行调优的基准测试的性能来评估我们的框架:一个Intel P4 Xeon处理器集群、一个Cell处理器集群和一个Sony Playstation3集群。
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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