论文标题
使用木乃伊和十种机器学习方法的性能和功率建模和预测
Performance and Power Modeling and Prediction Using MuMMI and Ten Machine Learning Methods
论文作者
论文摘要
在本文中，我们使用建模和预测工具木乃伊（多个指标建模基础架构）和十种机器学习方法来建模和预测性能和功率，并比较其预测错误率。我们使用耐故障的线性代数代码和容忍故障的热分配代码来对Argonne National Laboratory的Cray XC40 Theta和IBM BG/Q Mira进行建模和预测研究，以及Intel Hastel Sandia国家实验室的Intel Haswell Shepard。我们的实验结果表明，在大多数情况下，使用木乃伊的性能和功率的预测错误率小于10％。基于运行时，节点功率，CPU功率和内存功率的模型，我们确定了与应用程序特征和目标体系结构相关的潜在优化工作的最重要的性能计数器，并且我们预测了潜在优化的理论结果。当我们使用木乃伊与10种机器学习方法进行比较的预测准确性时，我们观察到妈妈不仅可以在性能和功率上进行更准确的预测，而且还提出了性能如何影响性能和功率模型。这提供了一些有关如何微调应用和/或系统以提高能源效率的见解。
In this paper, we use modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and ten machine learning methods to model and predict performance and power and compare their prediction error rates. We use a fault-tolerant linear algebra code and a fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experiment results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. Based on the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential optimization efforts associated with the application characteristics and the target architectures, and we predict theoretical outcomes of the potential optimizations. When we compare the prediction accuracy using MuMMI with that using 10 machine learning methods, we observe that MuMMI not only results in more accurate prediction in both performance and power but also presents how performance counters impact the performance and power models. This provides some insights about how to fine-tune the applications and/or systems for energy efficiency.
