Automatic Performance Tuning of Parallel Mathematical Libraries

Ihab Salawdeh*, Anna Morajko, Eduardo César, Tomàs Margalef, Emilio Luque

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


Scientific and mathematical parallel libraries offer a high level of abstraction to programmers. However, their use involves a large number of decisions, such as choosing partitioning strategies, pre-conditioners, and solving strategies, which have a great impact on the performance of resulting applications. This work proposes a performance methodology for automatic tuning of applications written with the PETSc library. This methodology consists of strategies for: choosing the appropriate data representation and solving algorithms based on historical performance information and data mining techniques, distributing the workload among application processes, and taking advantage of the library memory pre-allocation capacities.

Original languageEnglish
Pages (from-to)407-414
Number of pages8
JournalAdvances in Parallel Computing
Issue number1
Publication statusPublished - 1 Jan 2010


  • Mathematical Libraries Performance
  • Performance Models


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