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-Mishra
Jaemoon Lee
Anand Rangarajan
Lipeng Wan
Nicolas Vidal
Qing Liu
Ana Gainaru
N. Podhorszki
Rick Archibald
Sanjay Ranka
S. Klasky

Abstract
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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