h5bench: HDF5 I/O Kernel Suite for Exercising HPC I/O Patterns

Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputing systems. With massive amounts of data being 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 that are representative of current workloads on HPC systems. Towards 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 because of the parallel I/O library’s heavy usage in a wide variety of scientific applications running on supercomputing systems. The various dimensions of h5bench 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) and I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on a Cray system: Cori at NERSC using both the DataWarp burst buffer and a Lustre file system and Summit at Oak Ridge Leadership Computing Facility (OLCF) using a SpectrumScale file system. These performance measurements are using find performance bottlenecks, identify root causes of any poor performance, and optimize I/O performance. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful not only to the CUG community but also to the broader supercomputing community. Keywords—HDF5 benchmarks,