Optimizing Machine Learning Algorithms for High-Performance Computing Environments

Abstract

High-performance computing (HPC) environments, characterized by their vast computational resources, are increasingly crucial for running complex machine learning (ML) algorithms. This paper explores strategies for optimizing ML algorithms to fully leverage the capabilities of HPC systems. It reviews current methodologies, highlights best practices, and proposes novel techniques for improving computational efficiency and scalability. The study also presents empirical results demonstrating the effectiveness of these optimization strategies across various HPC platforms.