Predicting number of threads using balanced datasets for openMP regions
Predicting number of threads using balanced datasets for openMP regions
复制标题
使用 openMP 区域的平衡数据集预测线程数
DOI：
10.1007/s00607-022-01081-6
复制
发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Eduardo César
中科院分区：
文献类型：
--
作者：
J. Alcaraz;Ali TehraniJamsaz;Akashnil Dutta;A. Sikora;A. Jannesari;Joan Sorribes;Eduardo César
将机器学习纳入自动性能分析和调整工具是解决当前HPC应用程序异质性不断增长的有望途径，但是，这引入了生成并行应用程序执行的平衡数据集并处理自然失衡以优化性能参数。这项工作提出了一种整体方法，该方法整合了一种方法，用于构建OpenMP代码区域模式的平衡数据集和一种使用方式这种调整性能参数的数据集使用硬件性能计数器来表征给定区域的执行和相关性分析，以确定它是否涵盖了模式输入空间的唯一部分。当用于调整任何特定性能参数的模型时，我们已经探索了几种处理自然失衡数据集的方法使用它们来调整目的的方法。 OpenMP并行区域的线程。
Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced datasets of parallel applications’ executions and for dealing with natural imbalances for optimizing performance parameters. This work proposes a holistic approach that integrates a methodology for building balanced datasets of OpenMP code-region patterns and a way to use such datasets for tuning performance parameters. The methodology uses hardware performance counters to characterize the execution of a given region and correlation analysis to determine whether it covers an unique part of the pattern input space. Nevertheless, a balanced dataset of region patterns may become naturally imbalanced when used for training a model for tuning any specific performance parameter. For this reason, we have explored several methods for dealing with naturally imbalanced datasets for finding the appropriated way of using them for tuning purposes. Experimentation shows that the proposed methodology can be used to build balanced datasets and that such datasets, plus a combination of Random Forest and binary classification, can be used to train a model able to accurately tune the number of threads of OpenMP parallel regions.