Optimize High-Performance Computing Storage | CoreWeave

This post provides tactical strategies for optimizing storage performance for ML workloads on CoreWeave Cloud, building upon previous benchmarking. It details practical techniques including: implementing multi-level buffering and caching with considerations for system resources; effective prefetching strategies to keep GPUs fed without memory contention; optimizing data format (TFRecord, WebDataset) and organization (sharding) for efficient loading; pipeline optimization to overlap I/O, decompression, preprocessing, and GPU transfer; memory management and distributed coordination for cache contents and prefetch operations across clusters; and monitoring key metrics (I/O wait, throughput, latency, cache hit rates) and implementing error handling for reliable, consistent performance.