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

机译:自适应粒度和协调管理,可在多核系统中及时进行预取

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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.
机译:在过去的十年中,已经有各种用于硬件预取的技术可以改善系统性能。但是,不及时的预取可能会污染缓存并导致严重的性能下降。在这项工作中,我们介绍了一种自适应粒度和协调预取(AGP),它由粗粒度和细粒度的预提取机制组成,以为并行应用程序提供更好的缓存环境。 AGP着眼于度调整和位置选择,并试图将预取器对每个核心造成的影响降到最低。 AGP可以产生更及时的预取请求,从而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%)的性能提升。

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