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Performance Optimization of the 3D FDM Simulation of Seismic Wave Propagation on the Intel Xeon Phi Coprocessor Using the ppOpen-APPL/FDM Library

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High Performance Computing for Computational Science -- VECPAR 2014 (VECPAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8969))

Abstract

We evaluate the performance of a parallel 3D finite-difference method (FDM) simulation of seismic wave propagation using the Intel Xeon Phi coprocessor. Since a continued decrease in the byte/flop ratio of future machines is forecast, program optimization with a decrease byte/flop ratio was applied by fusing the original major kernel and omitting the storing and loading of intermediate variables. We confirm that 1) MPI/OpenMP hybrid parallel computing with hyper-threading is more efficient than pure MPI parallel computing and 2) the performance of the FDM simulation with a splitting of triple DO loops is 1.3 times faster than the modified code with triple DO loops, while no performance acceleration is achieved with a fused double DO-loop calculation. We consider that loop distribution optimization is effective for prefetching and the thread parallelization of each loop by its use and reuse on cache data.

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Acknowledgments

This work is supported by Core Research for Evolution Science and Technology (CREST), the Japan Science and Technology Agency (JST), Japan. We are grateful to Professor Tsuruoka at the Earthquake Research Institute, The University of Tokyo, for providing the Intel Xeon Phi coprocessor computing environment. We also thank our anonymous reviewers for a number of constructive comments for the revision of the manuscript.

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Correspondence to Futoshi Mori .

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© 2015 Springer International Publishing Switzerland

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Mori, F., Matsumoto, M., Furumura, T. (2015). Performance Optimization of the 3D FDM Simulation of Seismic Wave Propagation on the Intel Xeon Phi Coprocessor Using the ppOpen-APPL/FDM Library. In: Daydé, M., Marques, O., Nakajima, K. (eds) High Performance Computing for Computational Science -- VECPAR 2014. VECPAR 2014. Lecture Notes in Computer Science(), vol 8969. Springer, Cham. https://doi.org/10.1007/978-3-319-17353-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-17353-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17352-8

  • Online ISBN: 978-3-319-17353-5

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