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GPU Optimization of Large-Scale Eigenvalue Solver

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Numerical Mathematics and Advanced Applications ENUMATH 2017 (ENUMATH 2017)

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 126))

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Abstract

We present a GPU implementation of a large-scale eigenvalue solver as a part of the ELPA library. We describe the methodology of utilizing the GPU accelerators within an already well optimized MPI-based code. We present numerical results using two different HPC systems equipped with modern GPU accelerators and show the performance benefits of the GPU version.

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References

  1. T. Auckenthaler, V. Blum, H.-J. Bungartz, T. Huckle, R. Johanni, L. Krmer, B. Lang, H. Lederer, P.R. Willems, Parallel solution of partial symmetric eigenvalue problems from electronic structure calculations. Parallel Comput. 37, 783–794 (2011)

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  2. A. Marek, V. Blum, R. Johanni, V. Havu, B. Lang, T. Auckenthaler, A. Heinecke, H.-J. Bungartz, H. Lederer, The ELPA library - scalable parallel eigenvalue solutions for electronic structure theory and computational science. J. Phys. Condens. Matter 26, 213201 (2014)

    Article  Google Scholar 

  3. ELPA Library, http://elpa.mpcdf.mpg.de

  4. G.H. Golub, C.F.V. Loan, Matrix Computations (John Hopkins University Press, Baltimore, 2013)

    MATH  Google Scholar 

  5. ScaLAPACK - Scalable Linear Algebra PACKage, http://netlib.org/scalapack

  6. Matrix Algebra on GPU and Multicore Architectures, http://icl.utk.edu/magma

  7. CuBLAS Library, https://developer.nvidia.com/cublas

  8. Multi-Process Service, https://docs.nvidia.com/deploy/pdf/CUDA_Multi_Process_Service_Overview.pdf

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Acknowledgements

Part of this work is co-funded by BMBF grant 01IH15001 of the German Government.

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Authors and Affiliations

  1. Max Planck Computing and Data Facility, Garching bei München, Germany

    Pavel Kůs, Hermann Lederer & Andreas Marek

Authors
  1. Pavel Kůs
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  2. Hermann Lederer
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  3. Andreas Marek
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Corresponding author

Correspondence to Pavel Kůs .

Editor information

Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Florin Adrian Radu

  2. University of Bergen, Bergen, Norway

    Kundan Kumar

  3. University of Bergen, Bergen, Norway

    Inga Berre

  4. University of Bergen, Bergen, Norway

    Jan Martin Nordbotten

  5. Hasselt University, Diepenbeek, Belgium

    Iuliu Sorin Pop

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Kůs, P., Lederer, H., Marek, A. (2019). GPU Optimization of Large-Scale Eigenvalue Solver. In: Radu, F., Kumar, K., Berre, I., Nordbotten, J., Pop, I. (eds) Numerical Mathematics and Advanced Applications ENUMATH 2017. ENUMATH 2017. Lecture Notes in Computational Science and Engineering, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-96415-7_9

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