What Operations can be Performed Directly on Compressed Arrays, and with What Error?
Pages 254 - 262
Abstract
In response to the rapidly escalating data movement-related costs of computing with large matrices and multidimensional arrays, several lossy compression methods have been developed that help reduce the volume of data moved. Unfortunately, all these methods require the data to be decompressed before operating on it. In this work, we develop a lossy compressor for arbitrary-dimensional arrays called PyBlaz 1 that supports a dozen non-trivial operations directly on compressed data while also offering good compression ratios. PyBlaz is based on the PyTorch framework, and thus can be run on CPUs or GPUs without any code changes. We evaluate the efficacy of PyBlaz on datasets originating in three applications: comparing shallow-water simulation implementations, measuring statistics from MRI images, and detecting the scission point in plutonium fission data. Our results demonstrate that PyBlaz’s compressed-domain operations achieve good scalability while incurring controllable errors. To our best knowledge, this is the first such lossy compressor that supports compressed-domain operations in the realm of handling arbitrary-dimensional scientific datasets.
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Published In
November 2023
2180 pages
ISBN:9798400707858
DOI:10.1145/3624062
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
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Published: 12 November 2023
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SC-W 2023
SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
November 12 - 17, 2023
CO, Denver, USA
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