FPGA-based architecture to speed-up scientific computation in seismic applications

Authors:
Victor Medeiros;Rodrigo Rocha;Antonyus Pyetro Ferreira;Joao Paulo Barbosa;Abel Silva-Filho;Manoel Eusebio De Lima;Thomas Grosser;Wolfgang Rosenstiel
Affiliations:
Centre for Informatics, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, 50.740-560, Recife (PE), Brazil.;Centre for Informatics, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, 50.740-560, Recife (PE), Brazil.;Centre for Informatics, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, 50.740-560, Recife (PE), Brazil.;Centre for Informatics, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, 50.740-560, Recife (PE), Brazil.;Centre for Informatics, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, 50.740-560, Recife (PE), Brazil.;Centre for Informatics, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, 50.740-560, Recife (PE), Brazil.;Wilhelm-Schickard-Institute for Computer Science, University of Tubingen, Sand 13, 72076 Tubingen, Germany.;Wilhelm-Schickard-Institute for Computer Science, University of Tubingen, Sand 13, 72076 Tubingen, Germany
Venue:
International Journal of High Performance Systems Architecture
Year:
2012

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Abstract

Hardware accelerators like GPGPUs and FPGAs have been used as an alternative to conventional CPU architectures in scientific computing applications and have shown considerable speed-ups on them. In this context, this work presents an FPGA-based solution that explores efficiently the data reuse and spatial and time domain parallelism for the first computational stage of the reverse time migration (RTM) algorithm, the seismic modelling. We also implemented the same algorithm for some CPUs and GPGPU architectures and our results showed that an FPGA-based approach can be a feasible solution to improve performance. Experimental results showed similar performance when compared to the GPGPU and up to 28.91 times speed-up when compared to CPUs. In terms of energy efficiency, the FPGA is almost 23 times and 1.75 times more efficient than the CPU and GPGPU, respectively. We also discuss some other features and possible optimisations that can be included in the proposed architecture that can make this performance even better.