h5bench

Tue, 30 Jan 2024 10:15:00 -0700

h5bench is a suite of parallel I/O benchmarks or kernels representing I/O patterns that are commonly used in HDF5 applications on high performance computing systems. h5bench measures I/O performance from various aspects, including the I/O overhead, and observed I/O rate.

Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high-performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real-world applications, we have created h5bench, a set of I/O kernels that exercise HDF5 I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library’s heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (1D arrays, 2D meshes, 3D cubes), I/O modes (synchronous and asynchronous). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community.

h5bench / Reporting and Enhancing

The proposed work will include standardizing and enhancing the reports generated by the suite, and integrate additional I/O kernels (e.g., HACC-IO).

Topics: I/O HPC benchmarking
Skills: Python, C/C++, good communicator
Difficulty: Moderate
Size: Large (350 hours)
Mentors: Jean Luca Bez and Suren Byna

h5bench / Compression

The proposed work will focus on including compression capabilities into the h5bench core access patterns through HDF5 filters.

Topics: I/O HPC benchmarking, compression
Skills: C/C++, Python, HDF5
Difficulty: Moderate
Size: Large (350 hours)
Mentors: Jean Luca Bez and Suren Byna

compression | UCSC OSPO

h5bench

h5bench / Reporting and Enhancing

h5bench / Compression