Summary
The rapid development of GPU hardware has promoted scientific supercomputing, enabling exascale data production on heterogeneous supercomputing systems. With GPU dominance in heterogeneous computing, the cyberinfrastructure of GPU-based scientific data compressors is still maturing, and several gaps need to be addressed: existing frameworks lack adaptations to many scientific data analysis requirements; there are no user-friendly interfaces and off-the-shelf solutions for GPU-based scientific data compressors; and the compressors that support non-NVIDIA GPU architectures are very limited. This
