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Multidimensional Monte Carlo Integration on Clusters with Hybrid GPU-Accelerated Nodes

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Parallel Processing and Applied Mathematics (PPAM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8384))

Abstract

The aim of this paper is to show that the multidimensional Monte Carlo integration can be efficiently implemented on clusters with hybrid GPU-accelerated nodes using recently developed parallel versions of LCG and LFG pseudorandom number generators. We explain how to utilize multiple GPUs and all available cores of CPUs within a single node and how to extend computations on all available nodes of a cluster using MPI. The results of experiments performed on a Tesla-based GPU cluster are also presented and discussed.

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Authors and Affiliations

  1. Institute of Mathematics, Maria Curie–Skłodowska University, Pl. Marii Curie-Skłodowskiej 1, 20-031, Lublin, Poland

    Dominik Szałkowski & Przemysław Stpiczyński

Authors
  1. Dominik Szałkowski
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  2. Przemysław Stpiczyński
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Corresponding author

Correspondence to Dominik Szałkowski .

Editor information

Editors and Affiliations

  1. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Department of Computer Science, Knoxville, Tennessee, USA

    Jack Dongarra

  3. Institute of Computer and Information Science, Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

  4. Technical University of Denmark Informatics and Mathematical Modelling, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Szałkowski, D., Stpiczyński, P. (2014). Multidimensional Monte Carlo Integration on Clusters with Hybrid GPU-Accelerated Nodes. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2013. Lecture Notes in Computer Science(), vol 8384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55224-3_56

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  • DOI: https://doi.org/10.1007/978-3-642-55224-3_56

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55223-6

  • Online ISBN: 978-3-642-55224-3

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