Streamlining HDF5’s AI Workloads Benchmarking

IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 722–730

Abstract

Rapid adoption of artificial intelligence (AI) in scientific computing requires new tools to evaluate I/O performance effectively. HDF5 is one of the data formats frequently used not only in HPC but also in modern AI applications. However, existing benchmarks are insufficient to address the current challenges posed by AI workloads. This paper introduces an extension to the existing HDF5 benchmark, called h5bench, by incorporating the workload characteristics from the MLPerf Storage - DLIO benchmark. This extension allows users to test AI workloads together with traditional HPC benchmarks in the same context without the complexities of installing various machine learning libraries. Our experimental analysis demonstrates that the extension can replicate the existing I/O patterns with easily customizable configurations to perform various scaling tests.

Authors 5

1. Dlyaver Djebarov Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany

2. Radita Liem Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany

3. Sarah Neuwirth

   Johannes Gutenberg University Mainz

   Affiliation as printed

   Johannes Gutenberg University Mainz,Mainz,Germany

4. Jean Luca Bez

   Lawrence Berkeley National Laboratory

   Affiliation as printed

   Lawrence Berkeley National Laboratory,Berkeley,USA

5. Suren Byna

   The Ohio State University

   Affiliation as printed

   The Ohio State University,Columbus,USA