AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications

• DOI: 10.48550/arxiv.2307.09609
• Publication Date: 2023-01-01
• Authors: Daoce Wang; Jesus Pulido; Pascal Grosset; Jiannan Tian; Sian Jin; Houjun Tang; Jean Sexton; Sheng Di; Zarija Lukić; Kai Zhao; Bo Fang; Franck Cappello; James Ahrens; Dingwen Tao
• Keywords: Lossy compression; Adaptive Mesh Refinement

As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage 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 one most efficient approaches tackle latter issue. Despite their respective advantages, few attempts have been made investigate how AMR can function together. To this end, study presents a novel in-situ framework that employs HDF5 filter improve both I/O costs boost quality for applications. We implement our into AMReX evaluate on real-world applications, Nyx WarpX, Summit supercomputer. Experiments with 4096 CPU cores demonstrate AMRIC improves ratio by up 81X performance 39X over AMReX's original solution.