Please use this identifier to cite or link to this item: https://doi.org/10.1109/CLUSTR.2009.5289175
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dc.titleData mining analysis to validate performance tuning practices for HPL
dc.contributor.authorTan, T.Z.
dc.contributor.authorMong Goh, R.S.
dc.contributor.authorMarch, V.
dc.contributor.authorSee, S.
dc.date.accessioned2013-07-04T08:38:40Z
dc.date.available2013-07-04T08:38:40Z
dc.date.issued2009
dc.identifier.citationTan, 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. <a href="https://doi.org/10.1109/CLUSTR.2009.5289175" target="_blank">https://doi.org/10.1109/CLUSTR.2009.5289175</a>
dc.identifier.isbn9781424450121
dc.identifier.issn15525244
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41906
dc.description.abstractApplications 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CLUSTR.2009.5289175
dc.sourceScopus
dc.subjectData mining
dc.subjectHPL
dc.subjectPerformance modeling
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/CLUSTR.2009.5289175
dc.description.sourcetitleProceedings - IEEE International Conference on Cluster Computing, ICCC
dc.identifier.isiutNOT_IN_WOS
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