The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Authors: Daoce Wang (Indiana University), Jesus Pulido and Pascal Grosset (Los Alamos National Laboratory (LANL)), Jiannan Tian and Sian Jin (Indiana University), Houjun Tang and Jean Sexton (Lawrence Berkeley National Laboratory (LBNL)), Sheng Di (Argonne National Laboratory (ANL)), Kai Zhao (Florida State University), Bo Fang (Pacific Northwest National Laboratory (PNNL)), Zarija Lukić (Lawrence Berkeley National Laboratory (LBNL)), Franck Cappello (Argonne National Laboratory (ANL)), James Ahrens (Los Alamos National Laboratory (LANL)), and Dingwen Tao (Indiana University)
Abstract: As supercomputers advance toward exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences exponential growth. Adaptive Mesh Refinement (AMR) has emerged as an effective solution to address these two challenges. Concurrently, error-bounded lossy compression is recognized as one of the most efficient approaches to tackle the latter issue. Despite their respective advantages, few attempts have been made to investigate how AMR and error-bounded lossy compression can function together. To this end, this study presents a novel in-situ lossy compression framework that employs the HDF5 filter to improve both I/O costs and boost compression quality for AMR applications. We implement our solution into the AMReX framework and evaluate on two real-world AMR applications, Nyx and WarpX, on the Summit supercomputer. Experiments with 512 cores demonstrate that AMRIC improves the compression ratio by 81x and the I/O performance by 39x over AMReX's original compression solution.
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