The DOI matches its Crossref record.
Fast Lossless Compression of Scientific Floating-Point Data
Abstract
In scientific computing environments, large amounts of floating-point data often need to be transferred between computers as well as to and from storage devices. Compression can reduce the number of bits that need to be transferred and stored. However, the run-time overhead due to compression may be undesirable in high-performance settings where short communication latencies and high bandwidths are essential. This paper describes and evaluates a new compression algorithm that is tailored to such environments. It typically compresses numeric floating-point values better and faster than other algorithms do. On our data sets, it achieves compression ratios between 1.2 and 4.2 as well as compression and decompression throughputs between 2.8 and 5.9 million 64-bit double-precision numbers per second on a 3 GHz Pentium 4 machine.
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Cited by
Fixed-Rate Compressed Floating-Point Arrays
Lindstrom, P. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346458 · Cited by 676
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Blalock, D., Ortiz, J. J. G., Frankle, J., & Guttag, J. (2020) · arXiv · doi:10.48550/arxiv.2003.03033 · Cited by 560
Tao, D., Di, S., Chen, Z., & Cappello, F. (2017) · 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2017.115 · Cited by 292
Metadata
- Authors
- Ratanaworabhan, P., Ke, J., & Burtscher, M.
- Venue
- Data Compression Conference (DCC'06)
- Publisher
- IEEE
- Published
- 2006-04-06
- Pages
- 133-142
- ISSN
- 1068-0314 (ISSN-L)
- OpenAlex
- W2164566630
- Cited by
- 159
- References
- 0
- FWCI
- 4.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
