AREP: Adaptive Resource Efficient Prefetching for Maximizing Multicore Performance
AREP: Adaptive Resource Efficient Prefetching for Maximizing Multicore Performance
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AREP:自适应资源高效预取,可最大限度地提高多核性能
DOI:
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发表时间:
2015
期刊:
影响因子:
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通讯作者:
D. Black
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文献类型:
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作者:
Muneeb Khan;M. Laurenzano;Jason Mars;Erik Hagersten;D. Black
Modern processors widely use hardware prefetching to hide memory latency. While aggressive hardware prefetchers can improve performance significantly for some applications, they can limit the overall performance in highly-utilized multicore processors by saturating the offchip bandwidth and wasting last-level cache capacity. Co-executing applications can slowdown due to contention over these shared resources. This work introduces Adaptive Resource Efficient Prefetching (AREP) -- a runtime framework that dynamically combines software prefetching and hardware prefetching to maximize throughput in highly utilized multicore processors. AREP achieves better performance by prefetching data in a resource efficient way -- conserving offchip-bandwidth and last-level cache capacity with accurate prefetching and by applying cache-bypassing when possible. AREP dynamically explores a mix of hardware/software prefetching policies, then selects and applies the best performing policy. AREP is phase-aware and re-explores (at runtime) for the best prefetching policy at phase boundaries. A multitude of experiments with workload mixes and parallel applications on a modern high performance multicore show that AREP can increase throughput by up to 49% (8.1% on average). This is complemented by improved fairness, resulting in average quality of service above 94%.