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Falcons: Pushing the Speed-Ratio Limits of Lossy Compression for Parallel Scientific Applications — arXiv:2610.41209

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41209
License (as archived): CC0-1.0
Archived: 2026-11-03T08:06:47.989Z
SHA-256 of the archived text: 1d100b4abb2e92436838ecdd09bc949ebd93a09a4834cb58fee56b0b46dccedf
Stable URL of this page: https://metraipolis.com/documento/dc_cd0fedbe057d

How to cite

Falcons: Pushing the Speed-Ratio Limits of Lossy Compression for Parallel Scientific Applications — arXiv:2610.41209 — arXiv (en), https://arxiv.org/abs/2610.41209 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd0fedbe057d (Metraipolis archive dc_cd0fedbe057d, sha256 1d100b4abb2e92436838ecdd09bc949ebd93a09a4834cb58fee56b0b46dccedf)

BibTeX

@misc{dccd0fedbe057d,
  title = {Falcons: Pushing the Speed-Ratio Limits of Lossy Compression for Parallel Scientific Applications — arXiv:2610.41209},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41209},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd0fedbe057d; sha256 1d100b4abb2e92436838ecdd09bc949ebd93a09a4834cb58fee56b0b46dccedf},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41209. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The extreme volume of scientific data has become a critical challenge for modern exa-scale scientific computing applications, and error-bounded lossy compression remains indispensable for scientific data management due to its high data reduction rate and controllable data fidelity. However, to ensure sufficient data reduction, existing compressors incur prohibitive computational overheads in real-world parallel scientific computing applications. To overcome the performance bottlenecks of error-bounded lossy compression, we present \textbf{Falcons}, a broadly deployable error-bounded lossy compressor that simultaneously achieves \textbf{high compression ratios with ultra-fast throughput, minimizing the overhead in parallel scientific applications}. Falcons integrates three key designs: memory-regularized interpolation with SIMD (Single Instruction Multiple Data) vectorization, distribution-adaptive lossless coding, and application-oriented optimizations for multiple parallelization schemes. Across our evaluations on cutting-edge supercomputers, Falcons clearly outperforms state-of-the-art compressors, simultaneously achieving up to 29\% compression ratio improvement and 255\% throughput improvement. In case studies of real-world applications, Falcons accelerates large-scale parallel data I/O and remote transfer by up to 44.6\% and 51.5\%, and provides 1.55$\times$ end-to-end speedup for the full WRF (Weather Research \& Forecasting) application workflow.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.DC, nuovi lavori del giorno).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2610.41209 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T08:00:08.587Z dalla pagina https://arxiv.org/list/cs.DC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).