Efficient Parallel I/O in HDF5 for Accelerator Computing
Project runs from 08/17/2020 to 04/30/2021
HDF5 is designed to store and manage high-volume and complex science data and has become the leading I/O middleware solution at DOE supercomputing centers. As upcoming exascale supercomputers are using accelerators, such as graphical processing units (GPUs), for improving the performance of computing, data must be moved efficiently between storage and accelerators. To perform efficient parallel I/O in accelerated computing nodes for moving data between multiple GPUs and the parallel file system using node-local storage devices and network interconnects, this project will extend asynchronous I/O. In this project, Dr. Michela Becci and her student will work with us in identifying I/O benchmarks representative of ECP applications and profiling their current performance. The project will then update the designs of asynchronous I/O for using GPUs and node-local storage on a compute node.