Proposal for comparison and measurement of parallel and distributed file systems for training ML models in the healthcare

Many scientific fields are increasingly relying on high-performance computing (HPC) to handle and analyze vast amounts of experimental data. At the same time, storage systems in modern HPC environments must adapt to different access patterns. These patterns involve frequent metadata operations, numerous small I/O requests, and randomized file access, whereas traditional parallel file systems have been optimized primarily for sequential and shared access to large files. In this research, we will compare GekkoFS and evaluate its performance against Lustre, a widely used parallel file system that meets the demanding requirements of HPC simulation environments. Our comparison aims to highlight the strengths and limitations of each system for training ML models in healthcare.