Smart, adaptive mapping of parallelism in the presence of external workload
Smart, adaptive mapping of parallelism in the presence of external workload
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DOI：
10.1109/cgo.2013.6495010
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发表时间：
2013-02
期刊：
影响因子：
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通讯作者：
M. Emani;Zheng Wang;M. O’Boyle
中科院分区：
文献类型：
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作者：
M. Emani;Zheng Wang;M. O’Boyle
鉴于多核在主流计算中的广泛应用，并行程序很少孤立执行，并且必须与其他竞争资源的应用程序共享平台。如果在映射程序时不考虑外部工作负载，会导致性能大幅下降。本文描述了一种自动方法，该方法将程序的编译时知识与动态运行时工作负载信息相结合，以确定程序到可用资源的最佳自适应映射。这种方法在不损害现有工作负载的情况下提高了目标应用程序的性能。这种方法在各种工作负载场景下的NAS和SpecOMP并行基准测试程序上进行了评估。平均而言，在12核机器上，我们的方法比最先进的方案性能提高了1.5倍。
Given the wide scale adoption of multi-cores in main stream computing, parallel programs rarely execute in isolation and have to share the platform with other applications that compete for resources. If the external workload is not considered when mapping a program, it leads to a significant drop in performance. This paper describes an automatic approach that combines compile-time knowledge of the program with dynamic runtime workload information to determine the best adaptive mapping of programs to available resources. This approach delivers increased performance for the target application without penalizing the existing workload. This approach is evaluated on NAS and SpecOMP parallel bench-mark programs across a wide range of workload scenarios. On average, our approach achieves performance gain of 1.5× over a state-of-art scheme on a 12 core machine.