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Common Midpoint (CMP) and Common Reflection Surface (CRS) are widely usedmethods for improving the signal-to-noise ratio in the field of seismicprocessing. These methods are computationally intensive and require highperformance computing. This paper optimizes these methods on the Sunwaymany-core architecture and implements large-scale seismic processing on theSunway Taihulight supercomputer. We propose the following three optimizationtechniques: 1) we propose a software cache method to reduce the overhead ofmemory accesses, and share data among CPEs via the register communication; 2)we re-design the semblance calculation procedure to further reduce the overheadof memory accesses; 3) we propose a vectorization method to improve theperformance when processing the small volume of data within short loops. Theexperimental results show that our implementations of CMP and CRS methods onSunway achieve 3.50x and 3.01x speedup on average compared tothe-state-of-the-art implementations on CPU. In addition, our implementation iscapable to run on more than one million cores of Sunway TaihuLight with goodscalability.

Massively Scaling Seismic Processing on Sunway TaihuLight Supercomputer