Conference proceeding
Towards Improving Reverse Time Migration Performance by High-speed Lossy Compression
2023 IEEE/ACM 23RD INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING, CCGRID, pp.651-661
01/01/2023
DOI: 10.1109/CCGrid57682.2023.00066
Abstract
Seismic imaging is an exploration method for estimating the seismic characteristics of the earth's sub-surface for geologists and geophysicists. Reverse time migration (RTM) is a critical method in seismic imaging analysis. It can produce huge volumes of data that need to be stored for later use during its execution. The traditional solution transfers the vast amount of data to peripheral devices and loads them back to memory whenever needed, which may cause a substantial burden to I/O and storage space. As such, an efficient data compressor turns out to be a very critical solution. In order to get the best overall RTM analysis performance, we develop a novel hybrid lossy compression method (called HyZ), which is not only fairly fast in both compression and decompression but also has a good compression ratio with satisfactory reconstructed data quality for post hoc analysis. We evaluate several state-of-the-art error-controlled lossy compression algorithms (including HyZ, BR, SZx, SZ, SZ-Interp, ZFP, etc.) in a supercomputer. Experiments show that HyZ not only significantly improves the overall performance for RTM by 6.29 similar to 6.60x but also obtains fairly good qualities for both RTM single snapshots and the final stacking image.
Details
- Title: Subtitle
- Towards Improving Reverse Time Migration Performance by High-speed Lossy Compression
- Creators
- Yafan Huang - University of IowaKai Zhao - University of Alabama at BirminghamSheng Di - Argonne National LaboratoryGuanpeng Li - University of IowaMaxim Dmitriev - Saudi AramcoThierry-Laurent D. Tonellot - Saudi AramcoFranck Cappello - University of Iowa
- Contributors
- Y Simmhan (Editor)I Altintas (Editor)A L Varbanescu (Editor)P Balaji (Editor)A S Prasad (Editor)L Carnevale (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- 2023 IEEE/ACM 23RD INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING, CCGRID, pp.651-661
- Publisher
- IEEE
- DOI
- 10.1109/CCGrid57682.2023.00066
- Number of pages
- 11
- Grant note
- DE-AC02-06CH11357 / U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR); Czech Academy of Sciences ARAMCO OAC-2003709; OAC-2104023; OAC-2211538; OAC-2211539/2247060 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2023
- Academic Unit
- Computer Science
- Record Identifier
- 9984455947002771
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