@inproceedings{cc592cfa9bc048339b0a5982fb2e2a51,
title = "AMRIC: A Novel in Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications",
abstract = "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 81× and the I/O performance by up to 39× over AMReX's original compression solution.",
keywords = "AMR, I/O, lossy compression, performance",
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 Kai Zhao and Bo Fang and Zarija Luki{\'c} and Franck Cappello and James Ahrens and Dingwen Tao",
note = "This work (LA-UR-23-24096) has been authored by employees of Triad National Security, LLC, which operates Los Alamos National Laboratory under Contract No. 89233218CNA000001 with the U.S. Department of Energy (DOE) and National Nuclear Security Administration (NNSA). The material was supported by the U.S. DOE Office of Science (SC), Office of Advanced Scientific Computing Research (ASCR), under contracts DE-AC02-06CH11357 and DE-AC02-05CH11231, and under award 66150: “CENATE - Center for Advanced Architecture Evaluation”. The Pacific Northwest National Laboratory is operated by Battelle for the U.S. Department of Energy under contract DE-AC05-76RL01830. This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of the DOE SC and NNSA. This work was also supported by NSF awards 2003709, 2303064, 2104023, 2247080, 2311875, 2311876, 2312673. This research used resources of the National Energy Research Scientific Computing Center, a DOE SC User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.; 2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 ; Conference date: 12-11-2023 Through 17-11-2023",
year = "2023",
month = nov,
day = "12",
doi = "10.1145/3581784.3613212",
language = "English (US)",
series = "Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023",
address = "United States",
}