Adaptive granularity and coordinated management for timely prefetching in multi-core systems

Chia-Jung Chang, Yin-Chi Peng, Chien-Chih Chen, Tien-Fu Chen, P. Yew
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引用次数: 0

Abstract

For the last decade, there have been varying techniques for hardware prefetching to improve the system performance. However, untimely prefetching may pollution caches and resulting into significant performance degradation. In this work, we introduce an Adaptive Granularity and coordinated Prefetching (AGP) that consists of a coarse-grained and fine-grained prefetched mechanism to provide a better caching environment for parallel applications. AGP targets on the degree-adjusting and location-choosing and tries to minimize the influence caused by prefetcher for each core. AGP could produce more timely prefetched requests reducing the cache pollutions and contentions. Across a variety of PARSEC benchmarks, AGP can contribute 6.5% (up to 36%) of performance improvement on a 4-core multicore system compared to the non-prefetching.
多核系统中及时预取的自适应粒度和协调管理
在过去的十年中,为了提高系统性能,出现了各种各样的硬件预取技术。然而,不及时的预取可能会污染缓存并导致显著的性能下降。在这项工作中,我们引入了一种自适应粒度和协调预取(AGP),它由粗粒度和细粒度预取机制组成,为并行应用程序提供更好的缓存环境。AGP以度调整和位置选择为目标,尽量减少预取器对每个核的影响。AGP可以产生更及时的预取请求,减少缓存污染和争用。在各种PARSEC基准测试中,与非预取相比,AGP可以在4核多核系统上贡献6.5%(最高36%)的性能改进。
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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