Accurate phase-level cross-platform power and performance estimation
Accurate phase-level cross-platform power and performance estimation
复制标题
准确的相级跨平台功耗和性能估计
DOI：
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发表时间：
2016
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通讯作者：
A. Gerstlauer
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作者：
Xinnian Zheng;L. John;A. Gerstlauer
快速准确的性能和功率预测是硬件和软件共同开发的关键挑战。传统的基于分析或基于模拟的方法通常太不准确或缓慢。在这项工作中，我们提出了Lacross，这是一种基于学习的新型，分析性的跨平台预测框架，可在目标硬件平台上快速准确地估算时间变化的软件性能和功耗。我们采用了一种基于阶段的方法，在该方法中，学习算法合成了分析代理模型，该模型可以从每个程序阶段中从主机上的硬件计数器测量获得的性能统计数据中预测工作量的性能和功能。我们的学习方法依赖于使用目标参考模型或实际硬件的一次性培训阶段。我们将方法应用于Spec 2006，SD-VB和Mibench的35个基准。结果平均表明，以超过500 mIP的速度预测细粒性能和功率迹线的预测准确性超过97％。
Fast and accurate performance and power prediction is a key challenge in co-development of hardware and software. Traditional analytical or simulation-based approaches are often too inaccurate or slow. In this work, we propose LACross, a novel learning-based, analytical cross-platform prediction framework that provides fast and accurate estimation of time-varying software performance and power consumption on a target hardware platform. We employ a fine-grained phase-based approach, where the learning algorithm synthesizes analytical proxy models that predict the performance and power of the workload in each program phase from performance statistics obtained through hardware counter measurements on the host. Our learning approach relies on a one-time training phase using a target reference model or real hardware. We applied our approach to 35 benchmarks from SPEC 2006, SD-VBS and MiBench. Results show on average over 97% prediction accuracy for predicting both fine-grain performance and power traces at speeds of over 500 MIPS.