As supercomputers advance towards 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 4096 CPU cores demonstrate that AMRIC improves the compression ratio by up to 81X and the I/O performance by up to 39X over AMReX's original compression solution.
@article{arxiv.2307.09609,
title = {AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications},
author = {Daoce Wang and Jesus Pulido and Pascal Grosset and Jiannan Tian and Sian Jin and Houjun Tang and Jean Sexton and Sheng Di and Zarija Lukić and Kai Zhao and Bo Fang and Franck Cappello and James Ahrens and Dingwen Tao},
journal= {arXiv preprint arXiv:2307.09609},
year = {2023}
}