High performance implementation of RTM seismic modeling on FPGAs: Architecture, arithmetic and power issues

Abstract

This work presents a case study in the oil and gas industry, namely the FPGA implementation of the 2D reverse timing migration (RTM) seismic modeling algorithm. These devices have been largely used as accelerators in scientific computing applications that require massive data processing, large parallel machines, huge memory bandwidth and power. The RTM algorithm enables you to directly solve the acoustic and elastic waves problems with precision in complex geological structures, demanding a high computational power. To face such challenges we suggest strategies such as reduced arithmetic precision, based on fixed-point numbers, and a highly parallel architecture are suggested. The effects of such reduced precision for storage/processing data are analyzed in this chapter through signal-noise ratio (SRN) and universal image quality index (UIQI) metrics. The results show that SRN higher than 50dB can be considered acceptable for a migrated image with 15 bits word size. A special stream-processing architecture aiming to implement the best possible data reuse for the algorithm is also presented. It was implemented by an FIFO-based cache in the internal memory of the FPGA. A temporal pipeline structure has also been developed, allowing that multiple time steps to be performed at the same time. The main advantage of this approach is the ability to keep the same memory bandwidth needs of processing just one time step. The number of time steps processed at the same time is limited by the amount of FPGA internal memory and logic blocks. The algorithm was implemented on an Altera Stratix 260E, with 16 processing elements (PEs). The FPGA was 29 times faster than the CPU and only 13% slower than the GPGPU. In terms of power consumption, the CPU+FPGA was 1.7 times more efficient than the GPGPU system.