Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in decompression as multidimensional partial-sum computation and propose a fine-grained Lorenzo reconstruction algorithm for GPU architectures. (3) We carefully optimize each of CUSZ kernels by leveraging state-of-the-art CUDA parallel primitives. (4) We evaluate CUSZ+ using seven real-world HPC application datasets on V100 and A100 GPUs. Experiments show CUSZ+ improves the compression throughputs and ratios by up to 18.4× and 5.3×, respectively, over CUSZ on the tested datasets.
|Title of host publication||Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021|
|Number of pages||11|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Cluster Computing, Cluster 2021 - Virtual, Portland, United States|
Duration: Sep 7 2021 → Sep 10 2021
|Name||Proceedings - IEEE International Conference on Cluster Computing, ICCC|
|Conference||2021 IEEE International Conference on Cluster Computing, Cluster 2021|
|Period||9/7/21 → 9/10/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations—the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering, and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357. This work was also supported by the National Science Foundation under Grants OAC-2042084, OAC-2034169, OAC-2003709, and CCF-1619253.
ASJC Scopus subject areas
- Hardware and Architecture
- Signal Processing