TY - GEN
T1 - Towards Improving Reverse Time Migration Performance by High-speed Lossy Compression
AU - Huang, Yafan
AU - Zhao, Kai
AU - Di, Sheng
AU - Li, Guanpeng
AU - Dmitriev, Maxim
AU - Tonellot, Thierry Laurent D.
AU - Cappello, Franck
N1 - This research was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR), under contract DE-AC02-06CH11357,and supported by the National Science Foundation under Grant OAC-2003709, OAC-2104023, OAC-2211538, and OAC-2211539/2247060. We acknowledgethe computing resources provided on Bebop (operated by Laboratory Computing Resource Center at Argonne) and on Theta and JLSE (operated by Argonne Leadership Computing Facility). This research was also supported by ARAMCO.
PY - 2023
Y1 - 2023
N2 - 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∼6.60× but also obtains fairly good qualities for both RTM single snapshots and the final stacking image.
AB - 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∼6.60× but also obtains fairly good qualities for both RTM single snapshots and the final stacking image.
KW - Lossy Compression
KW - Performance
KW - Reverse Time Migration
KW - Seismic Imaging
UR - http://www.scopus.com/inward/record.url?scp=85166297273&partnerID=8YFLogxK
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U2 - 10.1109/CCGrid57682.2023.00066
DO - 10.1109/CCGrid57682.2023.00066
M3 - Conference contribution
AN - SCOPUS:85166297273
T3 - Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023
SP - 651
EP - 661
BT - Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023
A2 - Simmhan, Yogesh
A2 - Altintas, Ilkay
A2 - Varbanescu, Ana-Lucia
A2 - Balaji, Pavan
A2 - Prasad, Abhinandan S.
A2 - Carnevale, Lorenzo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023
Y2 - 1 May 2023 through 4 May 2023
ER -