Abstract

The performance modeling of a parallel application is crucial for the better use of the HPC resources. However, certain scientific applications exhibit irregular performance characteristics, posing challenges in accurately modeling their behavior. This irregularity primarily arises from these applications' non-deterministic computation and communication patterns. This article introduces a performance modeling methodology designed for irregular parallel applications based on the PAS2P methodology. The PAS2P tool generates an application signature and utilizes it to analyze and predict performance. Our approach is based on process-based data analysis to characterize these applications according to the behavior of individual processes, proposing a model to group processes at the time of signature construction. This model allowed us to obtain a reduced number of phases and weights in a limited time, allowing us to characterize the application.