Preprint
HoSZp: An Efficient Homomorphic Error-bounded Lossy Compressor for Scientific Data
arXiv.org
Cornell University
08/21/2024
DOI: 10.48550/arxiv.2408.11971
Abstract
Error-bounded lossy compression has been a critical technique to significantly reduce the sheer amounts of simulation datasets for high-performance computing (HPC) scientific applications while effectively controlling the data distortion based on user-specified error bound. In many real-world use cases, users must perform computational operations on the compressed data (a.k.a. homomorphic compression). However, none of the existing error-bounded lossy compressors support the homomorphism, inevitably resulting in undesired decompression costs. In this paper, we propose a novel homomorphic error-bounded lossy compressor (called HoSZp), which supports not only error-bounding features but efficient computations (including negation, addition, multiplication, mean, variance, etc.) on the compressed data without the complete decompression step, which is the first attempt to the best of our knowledge. We develop several optimization strategies to maximize the overall compression ratio and execution performance. We evaluate HoSZp compared to other state-of-the-art lossy compressors based on multiple real-world scientific application datasets.
Details
- Title: Subtitle
- HoSZp: An Efficient Homomorphic Error-bounded Lossy Compressor for Scientific Data
- Creators
- Tripti AgarwalSheng DiJiajun HuangYafan HuangGanesh GopalakrishnanRobert UnderwoodKai ZhaoXin LiangGuanpeng LiFranck Cappello
- Resource Type
- Preprint
- Publication Details
- arXiv.org
- DOI
- 10.48550/arxiv.2408.11971
- eISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 08/21/2024
- Academic Unit
- Computer Science
- Record Identifier
- 9984699519102771
Metrics
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