  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 paper 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 3.50x and 3.01x 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.
