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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
Oak Ridge National Laboratory, USA
arXiv:2401.05994 [cs.CV], (11 Jan 2024)

@article{Gong_2023,

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

   volume={24},

   ISSN={2352-7110},

   url={http://dx.doi.org/10.1016/j.softx.2023.101590},

   DOI={10.1016/j.softx.2023.101590},

   journal={SoftwareX},

   publisher={Elsevier BV},

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

   year={2023},

   month={dec},

   pages={101590}

}

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.
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