异构计算环境下的三维 Kirchhoff叠前深度偏移混合域并行算法

The 3D Kirchhoff prestack depth migration (KPSDM) is the most important depth-domain imaging method in the seismic data processing. Currently seismic data size of a single survey exceeds 100TB, and will increase to more than 1PB in the near future. Considering the continuous increasing survey sizes and the introduction of programmable graphic process unit (GPU), the conventional parallel strategy is no longer appropriate for the large-scale heterogeneous processing clusters. In this paper, we propose a practical hybrid domain parallel KPSDM algorithm based on two-level decomposition including imaging space and seismic data. The algorithm eliminates the dependency among tasks. In a heterogeneous environment, we implement the computing part on GPU and design a "dynamic and asynchronous" task allocation policy to achieve load balancing on heterogeneous computing system. Because KPSDM, as part of its execution, usually requires repeated access to huge seismic data and a large amount of travel time tables, the scalability is always limited by the shared storage maximum throughput. To solve the scalability problem, we build a distributed cache system using the local storage for a KPSDM job spans. It can provide a very high aggregate data bandwidth to supply seismic data and travel time table to a running task timely. The KPSDM implementation can obtain close to linear speedup when it processes real seismic data on a 256-node cluster.