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Article Dans Une Revue Computing and Software for Big Science Année : 2018

Polynomial data compression for large-scale physics experiments

Résumé

The next generation of research experiments will introduce a huge data surge to continuously increasing data production by current experiments. This data surge necessitates efficient compression techniques. These compression techniques must guarantee an optimum trade-off between compression rate and the corresponding ratio of compression to decompression speed without affecting the data integrity. This work presents a lossless compression algorithm to compress physics data generated by astronomy, astrophysics and particle physics experiments. The developed algorithms have been tuned and tested on a real-use case: the next-generation, ground-based, high-energy gamma ray observatory, Cherenkov Telescope Array, requiring important compression performance. As a stand-alone method, the proposed compression method is very fast and reasonably efficient. Alternatively, applied as a pre-compression algorithm, it can accelerate common methods like the Lempel–Ziv–Markov chain algorithm (LZMA), keeping close performance.
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Dates et versions

hal-01960337 , version 1 (08-12-2023)

Identifiants

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Pierre Aubert, Thomas Vuillaume, Gilles Maurin, Jean Jacquemier, Giovanni Lamanna, et al.. Polynomial data compression for large-scale physics experiments. Computing and Software for Big Science, 2018, 2 (1), pp.6. ⟨10.1007/s41781-018-0010-3⟩. ⟨hal-01960337⟩
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