PRISM: Evaluating POSIX Storage Systems for AI Research Workflows

2026-07-23

AI Summary

Existing storage benchmarks predominantly emphasize peak performance and fail to capture the dynamic, heterogeneous, and researcher-efficiency-driven POSIX file system usage patterns prevalent in AI research. To address this gap, this work proposes PRISM, the first storage evaluation framework tailored to AI research workflows. PRISM integrates representative I/O patterns—including data loading, distributed checkpointing, and interactive debugging—while preserving POSIX compatibility and reflecting the burstiness and diversity of real-world workloads. Leveraging realistic workload modeling, PRISM empirically evaluates systems such as Lustre and NFS, revealing that flash-accelerated NFS can achieve up to three times the performance of Lustre in distributed checkpointing scenarios, thereby offering critical insights for storage selection in GPU-accelerated clusters.

Abstract

The rapid advancement of AI research is driven by massive investments in GPU clusters, yet the critical role of storage systems in enabling efficient research workflows is often overlooked. Unlike traditional HPC workloads, AI research prioritizes researcher productivity and ease of iteration. Practitioners rely on POSIX compliant file systems for seamless prototyping, debugging, and experimentation before scaling to specialized storage backends. The primary selection criterion is therefore not peak throughput alone, but rather performance within a POSIX compatible, researcher friendly interface. However, existing benchmarks evaluate storage systems exclusively on peak performance and fail to capture the bursty, heterogeneous IO patterns characteristic of real world AI research - where workflows are dynamic, continuously evolving spanning all stages of research. We introduce PRISM, an evaluation framework that reproduces representative AI research workloads - spanning data ingestion, checkpoint IO, and developer workflows to assess and qualify POSIX storage systems along both usability and performance dimensions on GPU clusters. Using PRISM we were able to compare Lustre and NFS based POSIX storage systems across multiple research workload dimensions and select the appropriate storage solution for different environments. As a specific case study in our environment we observed that a flash backed NFS solution outperformed the flash backed Lustre solution by up to 3x for the distributed checkpoint load usecase which helped us make an informed cluster design

Problem

• POSIX storage
• AI research workflows
• I/O patterns
• storage evaluation
• researcher productivity

Innovation

• PRISM
• POSIX storage
• AI research workflows
• heterogeneous I/O
• storage benchmarking