Scaling Reverse Time Migration Performance through Reconfigurable Dataflow Engines

Haohuan Fu, Lin Gan, Robert G. Clapp, Huabin Ruan, Oliver Pell, Oskar Mencer, Michael J. Flynn, Xiaomeng Huang, Guangwen Yang

Abstract: Seismic migrations dominate about 90 percent of the computation cycles in the oil and gas industry. With the demand to handle high-density data and more complicated physics models, migration applications always call for more computing power, and they adopt new architectures quickly. Current multicore and many-core architectures have significantly improved the density of computational resources within a chip, but they also have made memory bandwidth a bottleneck that stops the scaling of performance over the increased number of cores. In this article, the authors present their reverse time migration design based on reconfigurable data-flow engines. Combining both algorithmic and architectural optimizations, they manage to achieve a balanced utilization of various resources (computational logic, local buffers, memory bandwidth, and so on) in the system, with none of them becoming the performance bottleneck. Their data-flow design provides performance equivalent to 72 Intel CPU cores, and achieves 10 times higher power efficiency than the multicore CPU architecture.