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Optimization of Parallel FDTD Computations Based on Structural Redeployment of Macro Data Flow Nodes

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Parallel Processing and Applied Mathematics (PPAM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3911))

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Abstract

This paper shows methodology, which enables profiling macro data flow graphs (MDFG) that represent computation and communication patterns for the Finite Difference Time Domain (FDTD) problem in irregular computational areas. MDFG optimization is performed in three phases: simulation area partitioning with generation of initial MDFG, macro data nodes merging with static load balancing to obtain given number of macro nodes and communication optimization to minimize (balance) inter-node data transmissions, computational cells redeployment to take into account computational system restrictions. Efficiency of computations for several communication systems (MPI, RDMA RB, SHMEM) is discussed. Experimental results obtained by simulation are presented.

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Author information

Authors and Affiliations

  1. Polish-Japanese Institute of Information Technology, 86 Koszykowa Str., 02-008, Warsaw, Poland

    Adam Smyk

  2. Institute of Computer Science, Polish Academy of Sciences, 21 Ordona Str., 01-237, Warsaw, Poland

    Marek Tudruj

Authors
  1. Adam Smyk
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  2. Marek Tudruj
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Editor information

Editors and Affiliations

  1. Institute of Computational and Information Sciences, Czestochowa University of Technology, Poland

    Roman Wyrzykowski

  2. Computer Science Department,, University of Tennessee, 37996-3450, Knoxville, TN, USA

    Jack Dongarra

  3. Poznan Supercomputing and Networking Center, Poland

    Norbert Meyer

  4. Informatics & Mathematical Modeling, Technical University of Denmark, 2800, Lyngby, DK, Denmark

    Jerzy Waśniewski

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© 2006 Springer-Verlag Berlin Heidelberg

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Smyk, A., Tudruj, M. (2006). Optimization of Parallel FDTD Computations Based on Structural Redeployment of Macro Data Flow Nodes. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2005. Lecture Notes in Computer Science, vol 3911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752578_65

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  • DOI: https://doi.org/10.1007/11752578_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34141-3

  • Online ISBN: 978-3-540-34142-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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