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Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction

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Abstract

In this work, we carry out the parallelization of the single level Fast Multipole Method (FMM) for solving acoustic-scattering problems (using the Helmholtz equation) on distributed-memory GPGPU systems. With the aim of enlarging the scope of feasible simulations, the presented solution combines the techniques developed for our distributed-memory CPU solver with our shared-memory GPGPU solver. The performance of the developed solution is proved using two different GPGPU clusters: the first one consists of two workstations with NVIDIA GTX 480 GPUs linked by a Gigabit Ethernet network, and the second one comprises four nodes with NVIDIA Tesla M2090 GPUs linked by an Infiniband network.

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Acknowledgements

This work has been partially supported by “Ministerio de Ciencia e Innovación” from Spain/FEDER under the research projects TEC2011-24492/TEC (iSCAT) and TIN2010-14971, and by “Gobierno del Principado de Asturias” (PCTI)/ FEDER under project PC10-06. The Airbus A300 series geometry has been provided by the research project GRD1-2001-40147 financed by the European Union. Financial support (grant: UNOV-10-BECDOC) given by the University of Oviedo is acknowledged. Finally, many thanks are due to Microway Incorporated by the chance of using their Tesla MD SimCluster.

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Authors and Affiliations

  1. Departamento de Ingeniería Eléctrica, Electrónica, de Computadores y de Sistemas, Universidad de Oviedo, Gijón, Spain

    Miguel López-Portugués, Jesús A. López-Fernández, R. G. Ayestarán & Fernando Las-Heras

  2. Departamento de Informática, Universidad de Oviedo, Gijón, Spain

    José Ranilla

Authors
  1. Miguel López-Portugués
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  2. Jesús A. López-Fernández
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  3. José Ranilla
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  4. R. G. Ayestarán
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  5. Fernando Las-Heras
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Correspondence to José Ranilla.

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López-Portugués, M., López-Fernández, J.A., Ranilla, J. et al. Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction. J Supercomput 64, 17–27 (2013). https://doi.org/10.1007/s11227-012-0786-6

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  • DOI: https://doi.org/10.1007/s11227-012-0786-6

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