Please use this identifier to cite or link to this item: https://doi.org/10.1109/CLUSTR.2009.5289175
Title: Data mining analysis to validate performance tuning practices for HPL
Authors: Tan, T.Z.
Mong Goh, R.S.
March, V. 
See, S.
Keywords: Data mining
HPL
Performance modeling
Issue Date: 2009
Source: Tan, T.Z.,Mong Goh, R.S.,March, V.,See, S. (2009). Data mining analysis to validate performance tuning practices for HPL. Proceedings - IEEE International Conference on Cluster Computing, ICCC. ScholarBank@NUS Repository. https://doi.org/10.1109/CLUSTR.2009.5289175
Abstract: 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. © 2009 IEEE.
Source Title: Proceedings - IEEE International Conference on Cluster Computing, ICCC
URI: http://scholarbank.nus.edu.sg/handle/10635/41906
ISBN: 9781424450121
ISSN: 15525244
DOI: 10.1109/CLUSTR.2009.5289175
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