CAP:高能效推测多线程的临界性分析

James Tuck, Wei Liu, J. Torrellas
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引用次数: 8

摘要

虽然芯片多处理器(CMP)上的推测性多线程(SM)能够加速难以并行化的应用程序,但积极推测的功率低效率是一个问题。为了提高SMs电源效率,我们注意到并非在SMs环境中运行的所有任务都同样重要。为了利用这一见解,本文为SM开发了一个新颖的、广泛适用的任务临界性模型。本文还提出了一种新的体系结构CAP,它可以动态地构建任务关键度图,并用它来制定SM CMP中的调度决策。对SPECint, SPECfp和Olden应用程序的实验表明,在具有一个快核和三个慢核的CMP中,有CAP的D2平均是没有CAP的91-95%。此外,它仅为具有四个快速核心且没有CAP的CMP的77-91%的E D2。总的来说,我们认为任务临界调度是有益的。
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CAP: Criticality analysis for power-efficient speculative multithreading
While speculative multithreading (SM) on a chip multiprocessor (CMP) has the ability to speed-up hard-to- parallelize applications, the power inefficiency of aggressive speculation is a concern. To improve SMs power effeciency, we note that not all the tasks that are running in a SM environment are equally critical. To leverage this insight, this paper develops a novel, widely-applicable task-criticality model for SM. It also proposes CAP, a novel architecture that builds a task-criticality graph dynamically and uses it to make scheduling decisions in a SM CMP. Experiments with SPECint, SPECfp, and Olden applications show that, in a CMP with one fast core and three slow ones, the E D2 with CAP is, on average, 91-95% of that without. Moreover, it is only 77-91% of the E D2 of a CMP with four fast cores and no CAP. Overall, we argue that scheduling for task criticality is beneficial.
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