FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data

Conference · Wed Jan 01 00:00:00 EST 2020

DOI:https://doi.org/10.1109/IPDPS47924.2020.00065· OSTI ID:1804070

Underwood, Robert; Di, Sheng; Calhoun, Jon C.; Cappello, Franck

With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have been developed for years. None of the existing scientific floating-point lossy data compressors, however, support effective fixed-ratio lossy compression. Yet fixed-ratio lossy compression for scientific floating-point data not only compresses to the requested ratio but also respects a user-specified error bound with higher fidelity. In this paper, we present FRaZ: a generic fixed-ratio lossy compression framework respecting user-specified error constraints. The contribution is twofold. (1) We develop an efficient iterative approach to accurately determine the appropriate error settings for different lossy compressors based on target compression ratios. (2) We perform a thorough performance and accuracy evaluation for our proposed fixed-ratio compression framework with multiple state-of-the-art error-controlled lossy compressors, using several real-world scientific floating-point datasets from different domains. Experiments show that FRaZ effectively identifies the optimum error setting in the entire error setting space of any given lossy compressor. While fixed-ratio lossy compression is slower than fixed-error compression, it provides an important new lossy compression technique for users of very large scientific floating-point datasets.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science - Office of Workforce Development for Teachers and Scientists (WDTS); USDOE Office of Science - Graduate Student Research (SCGSR) Program; USDOE Office of Science (SC); USDOE Exascale Computing Project; USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1804070
• Resource Relation: Conference: 34th IEEE International Parallel and Distributed Processing Symposium, 05/18/20 - 05/22/20, New Orleans, LA, US
• Country of Publication: United States
• Language: English