MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910294684467200 |
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| author | Gong, Qian Chen, Jieyang Whitney, Ben Liang, Xin Reshniak, Viktor Banerjee, Tania Lee, Jaemoon Rangarajan, Anand Wan, Lipeng Vidal, Nicolas Liu, Qing Gainaru, Ana Podhorszki, Norbert Archibald, Richard Ranka, Sanjay Klasky, Scott |
| author_facet | Gong, Qian Chen, Jieyang Whitney, Ben Liang, Xin Reshniak, Viktor Banerjee, Tania Lee, Jaemoon Rangarajan, Anand Wan, Lipeng Vidal, Nicolas Liu, Qing Gainaru, Ana Podhorszki, Norbert Archibald, Richard Ranka, Sanjay Klasky, Scott |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring Gong, Qian Chen, Jieyang Whitney, Ben Liang, Xin Reshniak, Viktor Banerjee, Tania Lee, Jaemoon Rangarajan, Anand Wan, Lipeng Vidal, Nicolas Liu, Qing Gainaru, Ana Podhorszki, Norbert Archibald, Richard Ranka, Sanjay Klasky, Scott Computer Vision and Pattern Recognition Numerical Analysis 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. |
| title | MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring |
| topic | Computer Vision and Pattern Recognition Numerical Analysis |
| url | https://arxiv.org/abs/2401.05994 |