Modeling Parallel AI Applications for Performance Analysis on Cloud Environments

Abstract

In high-performance computing (HPC) environments, efficient execution of AI applications is critical for optimal performance and resource utilization. In this work, we extend the PAS2P methodology to AI applications through message passing on HPC Cloud systems, defining the AI Application Model to describe their performance behavior. This extension identifies phases within AI applications, enabling analysis to focus on these phases instead of the entire application. By concentrating on them, we can better evaluate AI application efficiency, providing insights into system performance and guiding future optimizations for large-scale AI tasks on HPC infrastructure