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Multi-Parameter Performance Modeling Based on Machine Learning with Basic Block Features

Hao, Meng; Zhang, Weizhe; Wang, Yiming; Li, Dong; Xia, Wen; Wang, Hao; Lou, Chen
Chapter
Accepted version
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URI
https://hdl.handle.net/11250/2649739
Date
2020
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  • Institutt for datateknologi og informatikk [7358]
  • Publikasjoner fra CRIStin - NTNU [41872]
Original version
10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00054
Abstract
Considering the increasing complexity and scale of HPC architecture and software, the performance modeling of parallel applications on large-scale HPC platforms has become increasingly important. It plays an important role in many areas, such as performance analysis, job management, and resource estimation. In this work, we propose a multi-parameter performance modeling and prediction framework called MPerfPred, which utilizes basic block frequencies as features and uses machine learning algorithms to automatically construct multi-parameter performance models with high generalization ability. To reduce the prediction overhead, we propose some feature-filtering strategies to reduce the number of features in the training stage and build a serial program called BBF collector for each target application to quickly collect feature values in the prediction stage. We demonstrate the use of MPerfPred on the TianHe-2 supercomputer with six parallel applications. Results show that MPerfPred with SVR achieves better prediction than other input parameter-based modeling methods. The average prediction error and average standard deviation of prediction errors of MPerfPred are 8.42% and 6.09%, respectively. In the prediction stage, the average prediction overhead of MPerfPred is less than 0.13% of the total execution time.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)

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