Name
Title | ||
|---|---|---|
Multi-Parameter Performance Modeling Based on Machine Learning with Basic Block Features |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00054 | 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) |
Keywords | DocType | ISBN |
performance modeling,parallel application,basic block feature,machine learning | Conference | 978-1-7281-4329-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| meng hao | 1 | 6 | 3.17 |
| Weizhe Zhang | 2 | 287 | 53.07 |
| Yiming Wang | 3 | 109 | 28.42 |
| Li, Dong | 4 | 764 | 48.56 |
| Wen Xia | 5 | 292 | 20.79 |
| Hao Wang | 6 | 16 | 3.28 |
| Chen Lou | 7 | 0 | 0.34 |