Massively Scaling Seismic Processing on Sunway TaihuLight Supercomputer

TL;DR

This paper presents optimized seismic processing methods for CMP and CRS on the Sunway TaihuLight supercomputer, achieving significant speedups and scalability through novel memory and computation techniques.

Contribution

It introduces three new optimization techniques tailored for Sunway architecture, enabling large-scale seismic processing with high efficiency.

Findings

Achieved 3.50x and 3.01x speedup over CPU implementations.

Successfully scaled to over one million cores with good performance.

Reduced memory access overhead and improved data sharing among cores.

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 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.