IO500基准测试的仓库级性能特征分析
Repository-Scale Performance Characterization of the IO500 Benchmark
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中文总结 AI 辅助
本研究对2019-2025年间294个IO500提交进行仓库级分析，揭示阶段间性能关系及规模敏感性，证明超越排行榜分析的价值。
中文摘要 AI 辅助
IO500基准测试为评估高性能计算（HPC）存储系统提供了共同基础，而其不断增长的提交仓库也为研究跨系统和跨时间的性能行为提供了机会。本研究对2019年至2025年间来自116个站点的294个重置后IO500提交进行了仓库级特征分析。我们将描述性和时间性分析与阶段级相关性、规模敏感性分析以及基准日志检查相结合，以调查由各个基准阶段所代表的性能特征如何与综合排名相关联。结果表明，测量相似I/O行为的阶段之间存在强相关性，但带宽与元数据性能之间的关系较弱且对规模敏感。时间性和日志级分析进一步揭示了仅从聚合分数中无法显现的信息。这些发现证明了分析IO500结果超越排行榜排名的价值，并指出了在使用社区基准数据进行比较性和纵向HPC性能分析时，关于系统规模、基准执行和仓库来源的重要考虑因素。
英文摘要
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.
发表机构
- University of Göttingen, GWDG, Germany（哥廷根大学，德国国家科研数据中心）
机构由 AI 辅助整理，请以论文原文为准。
