2026-04-13T00:03:41Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/4629462026-03-13T10:21:31Zcom_2072_98col_2072_378192

00925njm 22002777a 4500 dc Tirado, Felipe author Wong, Álvaro author Rexachs, Dolores author Luque, Emilio author 2022 Altres ajuts: acords transformatius de la UAB 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. Performance prediction Application performance analysis MPI parallel application Application signature Scalable performance analysis method for SPMD applications