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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes
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Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

机译：基于预测的多线程科学代码的性能适配

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摘要

Computing has recently reached an inflection point with the introduction of multi-core processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores, however in several domains users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications, and a runtime system which uses live program analysis to optimize applications dynamically. We describe a dynamic, phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven, phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8% simultaneous with an improvement in performance of 17.9%, resulting in energy savings of 26.7%.
机译：随着多核处理器的推出，计算最近达到了一个转折点。片上线程级并行度大约每两年就翻一番。并发性很自然地使程序可以通过调节活动内核的数量来交换性能以节省功率，但是在某些领域，用户不愿意牺牲性能来节省功率。我们提出了一种预测模型，用于识别经过良好调整的多线程科学应用程序中的高能效并发操作点，以及一个使用实时程序分析动态优化应用程序的运行时系统。我们描述了一个动态的，具有阶段意识的性能预测模型，该模型将多元回归技术与从硬件事件计数器收集的数据的运行时分析相结合，以找到并发的最佳操作点。使用我们的模型，我们开发了一种预测驱动的，具有阶段意识的运行时优化方案，该方案可以限制并发性，从而可以降低功耗，并可以在每个程序阶段的可伸缩性曲线的拐点处设置性能。预测的使用减少了搜索优化空间的开销，同时实现了接近最佳的性能和节能效果。对我们方法的全面评估显示，功耗降低了10.8％，性能提高了17.9％，从而节省了26.7％的能源。

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