Benchmarking Storage Systems for Machine Learning Workloads Using NIO Bench
Machine learning training workloads place unique demands on storage systems, yet most existing benchmarks focus on computational throughput rather than file system I/O behavior. We present a benchmarking framework, Neural I/O Benchmark (NIO Bench), that characterizes storage access patterns across six diverse ML model architectures: Language Transformers, Vision Transformers, Diffusion Models, Spiking Neural Networks, Artificial Neural Networks, and Reinforcement Learning. Our framework employs a two-layer tracing approach combining Python-level I/O hooks for semantic phase context with Linux strace for complete syscall coverage including DataLoader worker subprocesses. We evaluate all si...
| Authors | Jonathan W. Morris, Ionut Mistreanu, Connor Louie |
|---|---|
| Primary category | cs.PF |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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