MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
This addresses storage reduction, high-performance I/O, and in-situ data analysis for scientific computing, representing a novel method for a known bottleneck.
The paper tackles the problem of compressing floating-point scientific data with precise error control, presenting MGARD, a multigrid framework that achieves exceptional data compression and high-performance operations across diverse computing architectures.
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.