准确有效的微架构性能和功耗预测回归模型

ASPLOS XII Pub Date : 2006-10-23 DOI:10.1145/1168857.1168881
Benjamin C. Lee, D. Brooks
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引用次数: 501

摘要

我们提出回归建模作为一种有效的方法,用于准确预测在大型微架构设计空间中任何微处理器配置上执行的各种应用程序的性能和功耗。本文通过减少所需的仿真次数,并通过统计建模和推理更有效地利用仿真结果,解决了微架构仿真成本方面的基本挑战。具体来说,我们推导并验证了性能和功率的回归模型。这样的模型可以实现计算效率高的统计推断,只需要在联合微架构-应用程序设计空间中模拟500万分之一的点,同时在性能和功耗方面实现中位错误率低至4.1%和4.3%。尽管两种模型都达到了相似的精度,但准确性的来源却截然不同。我们对基线回归模型进行了优化,以获得(1)特定于应用的模型,以最大限度地提高性能预测的准确性;(2)仅利用微建筑设计空间中最相关的样本的区域功率模型,以最大限度地提高功率预测的准确性。评估对模型制定模拟样本数量的敏感性,我们发现从大约220亿个点的设计空间中少于4,000个样本是足够的。总的来说,我们的研究结果表明,通过回归模型对微建筑设计空间探索进行准确有效的统计推断具有重要潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accurate and efficient regression modeling for microarchitectural performance and power prediction
We propose regression modeling as an efficient approach for accurately predicting performance and power for various applications executing on any microprocessor configuration in a large microarchitectural design space. This paper addresses fundamental challenges in microarchitectural simulation cost by reducing the number of required simulations and using simulated results more effectively via statistical modeling and inference.Specifically, we derive and validate regression models for performance and power. Such models enable computationally efficient statistical inference, requiring the simulation of only 1 in 5 million points of a joint microarchitecture-application design space while achieving median error rates as low as 4.1 percent for performance and 4.3 percent for power. Although both models achieve similar accuracy, the sources of accuracy are strikingly different. We present optimizations for a baseline regression model to obtain (1) application-specific models to maximize accuracy in performance prediction and (2) regional power models leveraging only the most relevant samples from the microarchitectural design space to maximize accuracy in power prediction. Assessing sensitivity to the number of samples simulated for model formulation, we find fewer than 4,000 samples from a design space of approximately 22 billion points are sufficient. Collectively, our results suggest significant potential in accurate and efficient statistical inference for microarchitectural design space exploration via regression models.
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