Scaling Reverse Time Migration Performance through Reconfigurable Dataflow Engines

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.