Repository-Scale Performance Characterization of the IO500 Benchmark

2026-10-04

AI Summary

This study addresses the limitation of the IO500 benchmark, which emphasizes rankings while overlooking system scale, temporal evolution, and log details, by conducting a repository-level feature analysis of 294 submissions. Methodologically transcending conventional aggregated scoring, this work proposes a scale-aware longitudinal analysis framework that integrates descriptive statistics, temporal modeling, and phase-level correlation mining. The analysis reveals strong correlations among analogous I/O phases but weak coupling between bandwidth and metadata performance, alongside notable scale sensitivity. Furthermore, latent performance behavioral characteristics are extracted from execution logs. Ultimately, this research provides critical methodological foundations for leveraging community-submitted data to perform longitudinal performance evaluations of high-performance computing storage systems.

Abstract

The IO500 benchmark provides a common basis for evaluating high-performance computing (HPC) storage systems, while its growing submission repository also offers an opportunity to study performance behavior across systems and time. This work presents a repository-scale characterization of 294 post-reset IO500 submissions from 116 sites spanning 2019--2025. We combine descriptive and temporal analysis with phase-level correlation, scale-sensitivity analysis, and benchmark-log examination to investigate how performance characteristics represented by individual benchmark phases relate to composite rankings. The results show strong relationships among phases measuring similar I/O behavior, but weaker and scale-sensitive relationships between bandwidth and metadata performance. Temporal and log-level analyses further reveal information that is not apparent from aggregate scores alone. These findings demonstrate the value of analyzing IO500 results beyond leader-board rankings and identify important considerations concerning system scale, benchmark execution, and repository provenance when using community benchmark data for comparative and longitudinal HPC performance analysis.

Problem

• IO500 benchmark
• high-performance computing
• storage systems
• performance characterization
• benchmark repository

Innovation

• IO500 benchmark
• performance characterization
• high-performance computing
• scale-sensitivity analysis
• temporal analysis