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Data mining analysis to validate performance tuning practices for HPL

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4 Author(s)
Tuan Zea Tan ; Advanced Computing, Institute of High Performance Computing, Singapore ; Rick Siow Mong Goh ; Verdi March ; Simon See

Applications performance is a criterion for system evaluation, and hence performance tuning for these applications is of great interest. One such benchmark application is High Performance Linpack (HPL). Although guidelines exist for HPL tuning, validating these guidelines on various systems is a challenging task as a large number of configurations need to be tested. In this work, we use data mining analysis to reduce the number of configurations to be tested in validating the HPL tuning guidelines on the Ranger System. We validate that NB, P and Q are the three most important parameters to tune HPL, and that PMAP does not have a significant impact on HPL performance. We also validate the practice of tuning HPL at small N using data mining analysis. We find that the value of N selected for tuning should not be significantly smaller than the largest N that can fit into the system memory. Our results indicate that data mining could be further applied to application performance tuning.

Published in:

2009 IEEE International Conference on Cluster Computing and Workshops

Date of Conference:

Aug. 31 2009-Sept. 4 2009