With the ever-increasing volumes of data produced by today's large-scale scientific simulations, error-bounded lossy compression techniques have become critical: not only can they significantly reduce the data size but they also can retain high data fidelity for postanalysis. In this paper, we design a strategy to improve the compression quality significantly based on an optimized, hybrid prediction model. Our contribution is fourfold. (1) We propose a novel, transform-based predictor and optimize its compression quality. (2) We significantly improve the coefficient-encoding efficiency for the data-fitting predictor. (3) We propose an adaptive framework that can select the best-fit predictor accurately for different datasets. (4) We evaluate our solution and several existing state-of-the-art lossy compressors by running real-world applications on a supercomputer with 8,192 cores. Experiments show that our adaptive compressor can improve the compression ratio by 112∼165% compared with the second-best compressor. The parallel I/O performance is improved by about 100% because of the significantly reduced data size. The total I/O time is reduced by up to 60X with our compressor compared with the original I/O time.
|Title of host publication||Proceedings of SC 2019|
|Subtitle of host publication||The International Conference for High Performance Computing, Networking, Storage and Analysis|
|State||Published - Nov 17 2019|
|Event||2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019 - Denver, United States|
Duration: Nov 17 2019 → Nov 22 2019
|Name||International Conference for High Performance Computing, Networking, Storage and Analysis, SC|
|Conference||2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019|
|Period||11/17/19 → 11/22/19|
Bibliographical noteFunding Information:
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, and supported by the National Science Foundation under Grant No. 1619253. This work was also supported by National Science Foundation CCF 1513201. We acknowledge the computing resources provided on Bebop, which is operated by the Laboratory Computing Resource Center at Argonne National Laboratory.
© 2019 ACM.
- Compression performance
- Data dumping/loading
- Error-bounded lossy compression
- Rate distortion
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture