Lossy Scientific Data Compression With SPERR

被引：7
|
作者：
Li, Shaomeng [1 ]
Lindstrom, Peter [2 ]
Clyne, John [1 ]
机构：
[1] Natl Ctr Atmospher Res, Boulder, CO 80305 USA
[2] Lawrence Livermore Natl Lab, Lawrence, KS USA
来源：
2023 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM, IPDPS | 2023年
基金：
美国国家科学基金会;
关键词：
MULTILEVEL TECHNIQUES; IMAGE COMPRESSION; EFFICIENT; REDUCTION; QUANTIZATION;
D O I：
10.1109/IPDPS54959.2023.00104
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1007 / 1017
页数：11
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