zMesh: Theories and Methods to Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement
Document Type
Article
Publication Date
12-1-2022
Abstract
Scientific simulations on high-performance computing systems produce vast amounts of data that need to be stored and analyzed efficiently. Lossy compression significantly reduces the data volume by trading accuracy for performance. Despite the recent success of lossy compressions, such as ZFP and SZ, the compression performance is still far from being able to keep up with the exponential growth of data. This article aims to further take advantage of application characteristics, an area that is often under-explored, to improve the compression ratios of adaptive mesh refinement (AMR) - a widely used numerical solver that allows for an improved resolution in limited regions. We propose a level reordering technique zMeshto reduce the storage footprint of AMR applications. In particular, we group the data points that are mapped to the same or adjacent geometric coordinates such that the dataset is smoother and more compressible. Unlike the prior work where the compression performance is affected by the overhead of metadata, this work re-generates the restore recipe using a chained tree structure, thus involving no extra storage overhead for compressed data, which substantially improves the compression ratios. We further derive a mathematical proof that lays the foundation for our method. The results demonstrate that zMesh can improve the smoothness of data by 67.9% and 71.3% for Z-ordering and Hilbert, respectively. Overall, zMesh improves the compression ratios by up to 16.5% and 133.7% for ZFP and SZ, respectively. Despite that zMesh involves additional compute overhead for tree and restore recipe construction, we show that the cost can be amortized as the number of quantities to be compressed increases.
Identifier
85128698877 (Scopus)
Publication Title
IEEE Transactions on Parallel and Distributed Systems
External Full Text Location
https://doi.org/10.1109/TPDS.2022.3168386
e-ISSN
15582183
ISSN
10459219
First Page
3702
Last Page
3717
Issue
12
Volume
33
Grant
CCF-1718297
Fund Ref
National Science Foundation
Recommended Citation
Luo, Huizhang; Wang, Junqi; Liu, Qing; Chen, Jieyang; Klasky, Scott; and Podhorszki, Norbert, "zMesh: Theories and Methods to Exploring Application Characteristics to Improve Lossy Compression Ratio for Adaptive Mesh Refinement" (2022). Faculty Publications. 2468.
https://digitalcommons.njit.edu/fac_pubs/2468
