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
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.