Scalable performance analysis method for SPMD applications

Felipe Tirado*, Alvaro Wong, Dolores Rexachs, Emilio Luque

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review


The analysis of parallel scientific applications allows us to understand their computational and communication behavior. One way of obtaining performance information is through performance tools. One such tool is parallel application signatures for performance prediction (PAS2P), based on parallel application repeatability, focusing on performance analysis and prediction. The same resources that execute the parallel application are used to perform its analysis, creating a machine independent model of the application and identifying its common patterns. However, the analysis is costly in terms of execution time due to the high number of synchronization communications performed by PAS2P, degrading performance as the number of processes increases. To solve this problem, we propose a model that reduces data dependency between processes, reducing the number of communications performed by PAS2P in the analysis stage and taking advantage of the characteristics of single program, multiple sata applications. Our analysis proposal allows us to decrease the analysis time by 29 times when the application scales to 256 processes, while keeping error levels below 11% in the runtime prediction. It is important to mention that the analysis time is not considerably affected by increasing the number of application processes.

Original languageEnglish
Pages (from-to)19346-19371
Number of pages26
JournalJournal of Supercomputing
Issue number17
Publication statusPublished - Nov 2022


  • Application performance analysis
  • Application signature
  • MPI parallel application
  • Performance prediction


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