Abstract
In this paper, we discuss optimization of numerical computations of the FDTD problem in multiprocessor environments. The use of a genetic algorithm to find the best program macro data flow graph (MDFG) partition for a given FDTD problem for execution by a set of processors is presented. Different sub-graph merging actions are successively used in each step of the merging algorithm which starts from a program data flow graph representation. A special kind of chromosome represents consecutive steps of the graph partitioning algorithm to be applied to the current version of the macro data flow graph. To compare quality of individuals, we estimate the total execution time for each output MDF graph after applications of the actions specified in the algorithm, which they represent. To estimate efficiency of computations we used an architectural model which enables to represent parallel computations with 3 different communication protocols (MPI, RDMA RB, SHMEM). Experimental results obtained by simulation are presented.
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Smyk, A., Tudruj, M. (2008). Optimization of Parallel FDTD Computations Using a Genetic Algorithm. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_58
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DOI: https://doi.org/10.1007/978-3-540-68111-3_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68105-2
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