Scaling Deep Learning Models for High-Performance Computing Environments

Abstract

This paper examines the challenges and strategies involved in scaling deep learning models for HPC, focusing on parallelization techniques, hardware acceleration, memory optimization, and distributed computing. We explore methods such as data and model parallelism, advanced hardware (GPUs, TPUs), and hybrid models that combine cloud and on-premise HPC systems. The paper also highlights the importance of efficient resource allocation, load balancing, and fault tolerance in ensuring scalability and performance. Our findings suggest that successful scaling requires a holistic approach, integrating cutting-edge hardware, optimized software frameworks, and novel algorithmic techniques to fully harness the potential of HPC environments.