Lossy Scientific Data Compression With SPERR

As the need for data reduction in high-performance computing (HPC) continues to grow, we introduce a new and highly effective tool to help achieve this goal-SPERR. SPERR is a versatile lossy compressor for structured scientific data; it is built on top of an advanced wavelet compression algorithm, S...

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Vydáno v:Proceedings - IEEE International Parallel and Distributed Processing Symposium s. 1007 - 1017
Hlavní autoři: Li, Shaomeng, Lindstrom, Peter, Clyne, John
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2023
Témata:
Compression algorithms
Data compression
Distributed processing
High performance computing
Rate-distortion
ISSN:1530-2075
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Shrnutí:As the need for data reduction in high-performance computing (HPC) continues to grow, we introduce a new and highly effective tool to help achieve this goal-SPERR. SPERR is a versatile lossy compressor for structured scientific data; it is built on top of an advanced wavelet compression algorithm, SPECK, and provides additional capabilities valued in HPC environments. These capabilities include parallel execution for large volumes and a compression mode that satisfies a maximum point-wise error tolerance. Evaluation shows that in most settings SPERR achieves the best rate-distortion trade-off among current popular lossy scientific data compressors.
ISSN:1530-2075
DOI:10.1109/IPDPS54959.2023.00104