MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring

Qian Gong; Jieyang Chen; Ben Whitney; Xin Liang; Viktor Reshniak; Tania Banerjee; Jaemoon Lee; Anand Rangarajan; Lipeng Wan; Nicolas Vidal; Qing Liu; Ana Gainaru; Norbert Podhorszki; Richard Archibald; Sanjay Ranka; Scott Klasky

doi:10.1016/j.softx.2023.101590

MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring

Qian Gong
, Jieyang Chen
, Ben Whitney
, Xin Liang
, Viktor Reshniak
, Tania Banerjee
, Jaemoon Lee
, Anand Rangarajan
, Lipeng Wan
, Nicolas Vidal
, Qing Liu
, Ana Gainaru
, Norbert Podhorszki
, Richard Archibald
, Sanjay Ranka
, Scott Klasky

Producción científica: Article › revisión exhaustiva

22 Citas (Scopus)

Resumen

We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capability and precise error control, MGARD addresses a wide range of requirements, including storage reduction, high-performance I/O, and in-situ data analysis. It features a unified application programming interface (API) that seamlessly operates across diverse computing architectures. MGARD has been optimized with highly-tuned GPU kernels and efficient memory and device management mechanisms, ensuring scalable and rapid operations.

Idioma original	English
Número de artículo	101590
Publicación	SoftwareX
Volumen	24
DOI	https://doi.org/10.1016/j.softx.2023.101590
Estado	Published - dic 2023

Nota bibliográfica

Publisher Copyright:
© 2023

Financiación

This research was supported in part by the Exascale Computing Project CODAR (17-SC-20-SC) of the US Department of Energy (DOE), the DOE's Advanced Scientific Research Office (ASCR) research project SIRIUS-2, United States, and the DOE's RAPIDS-2 SciDAC project under contract number DE-AC05-00OR22725. In addition, this research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of DOE under Contract Numbers DE-AC05-00OR22725. This research was supported in part by the Exascale Computing Project CODAR (17-SC-20-SC) of the US Department of Energy (DOE), the DOE’s Advanced Scientific Research Office (ASCR) research project SIRIUS-2, United States , and the DOE’s RAPIDS-2 SciDAC project under contract number DE-AC05-00OR22725 . In addition, this research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of DOE under Contract Numbers DE-AC05-00OR22725.

Financiadores	Número del financiador
DOE's Advanced Scientific Research Office
DOE’s Advanced Scientific Research Computing Office
U.S. Department of Energy EPSCoR
Office of Science Programs
Advanced Scientific Computing Research	DE-AC05-00OR22725
Advanced Scientific Computing Research

ASJC Scopus subject areas

Software
Computer Science Applications

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10.1016/j.softx.2023.101590

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Gong, Q., Chen, J., Whitney, B., Liang, X., Reshniak, V., Banerjee, T., Lee, J., Rangarajan, A., Wan, L., Vidal, N., Liu, Q., Gainaru, A., Podhorszki, N., Archibald, R., Ranka, S., & Klasky, S. (2023). MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. SoftwareX, 24, Artículo 101590. https://doi.org/10.1016/j.softx.2023.101590

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AU - Whitney, Ben

AU - Liang, Xin

AU - Reshniak, Viktor

AU - Banerjee, Tania

AU - Lee, Jaemoon

AU - Rangarajan, Anand

AU - Wan, Lipeng

AU - Vidal, Nicolas

AU - Liu, Qing

AU - Gainaru, Ana

AU - Podhorszki, Norbert

AU - Archibald, Richard

AU - Ranka, Sanjay

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KW - I/O acceleration

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MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring

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ASJC Scopus subject areas

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