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Massively Scaling Seismic Processing on Sunway TaihuLight Supercomputer.

Authors :
Hu, Yongmin
Yang, Hailong
Luan, Zhongzhi
Gan, Lin
Yang, Guangwen
Qian, Depei
Source :
IEEE Transactions on Parallel & Distributed Systems; May2020, Vol. 31 Issue 5, p1194-1208, 15p
Publication Year :
2020

Abstract

Common Midpoint (CMP) and Common Reflection Surface (CRS) are widely used methods for improving the signal-to-noise ratio in the field of seismic processing. These methods are computationally intensive and require high-performance computing. This article optimizes these methods on the Sunway many-core architecture and implements large-scale seismic processing on the Sunway Taihulight supercomputer. We propose the following three optimization techniques: 1) we propose a software cache method to reduce the overhead of memory accesses, and share data among CPEs via the register communication; 2) we re-design the semblance calculation procedure to further reduce the overhead of memory accesses; 3) we propose a vectorization method to improve the performance when processing the small volume of data within short loops. The experimental results show that our implementations of CMP and CRS methods on Sunway achieve 3.50× and 3.01× speedup on average compared to the-state-of-the-art implementations on CPU. In addition, our implementation is capable to run on more than one million cores of Sunway TaihuLight with good scalability. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10459219
Volume :
31
Issue :
5
Database :
Complementary Index
Journal :
IEEE Transactions on Parallel & Distributed Systems
Publication Type :
Academic Journal
Accession number :
141418627
Full Text :
https://doi.org/10.1109/TPDS.2019.2962395