Fast Topology-Aware Lossy Data Compression with Full Preservation of Critical Points and Local Order
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910081592852480 |
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| author | Fallin, Alex Gorski, Nathaniel Agarwal, Tripti Wang, Bei Gopalakrishnan, Ganesh Burtscher, Martin |
| author_facet | Fallin, Alex Gorski, Nathaniel Agarwal, Tripti Wang, Bei Gopalakrishnan, Ganesh Burtscher, Martin |
| contents | Many scientific codes and instruments generate large amounts of floating-point data at high rates that must be compressed before they can be stored. Typically, only lossy compression algorithms deliver high-enough compression ratios. However, many of them provide only point-wise error bounds and do not preserve topological aspects of the data such as the relative magnitude of neighboring points. Even topology-preserving compressors tend to merely preserve some critical points and are generally slow. Our Local-Order-Preserving Compressor is the first to preserve the full local order (and thus all critical points), runs orders of magnitude faster than prior topology-preserving compressors, yields higher compression ratios than lossless compressors, and produces bit-for-bit the same output on CPUs and GPUs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_26968 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast Topology-Aware Lossy Data Compression with Full Preservation of Critical Points and Local Order Fallin, Alex Gorski, Nathaniel Agarwal, Tripti Wang, Bei Gopalakrishnan, Ganesh Burtscher, Martin Distributed, Parallel, and Cluster Computing Many scientific codes and instruments generate large amounts of floating-point data at high rates that must be compressed before they can be stored. Typically, only lossy compression algorithms deliver high-enough compression ratios. However, many of them provide only point-wise error bounds and do not preserve topological aspects of the data such as the relative magnitude of neighboring points. Even topology-preserving compressors tend to merely preserve some critical points and are generally slow. Our Local-Order-Preserving Compressor is the first to preserve the full local order (and thus all critical points), runs orders of magnitude faster than prior topology-preserving compressors, yields higher compression ratios than lossless compressors, and produces bit-for-bit the same output on CPUs and GPUs. |
| title | Fast Topology-Aware Lossy Data Compression with Full Preservation of Critical Points and Local Order |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2603.26968 |