MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
arXiv:2401.05994 · doi:10.1016/j.softx.2023.101590
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.
arXiv comment20 pages, 8 figures
References in corpus (3)
- TTHRESH: Tensor Compression for Multidimensional Visual Data
- Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
- COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
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- STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data
- A framework for compressing unstructured scientific data via serialization
- Lossless compression of simulated radio interferometric visibilities