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Simdist: a distribution system for easy parallelization of evolutionary computation

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Abstract

This article introduces Simdist, a software tool for parallel execution of evolutionary algorithms (EAs) in a master-slave configuration on cluster architectures. Clusters have become a cost-effective parallel solution, and the potential computational capabilities are phenomenal. However, the transition from traditional R&D on a personal computer to parallel development and deployment can be a major step. Simdist simplifies this transition considerably, by separating the task of distributing data across the cluster network from the actual EA-related processing performed on the master and slave nodes. Simdist is constructed in the vein of traditional Unix command line tools; it runs in a separate process and communicates with EA child processes via standard input and output. As a result, Simdist is oblivious to the programming language(s) used in the EA, and the EA is similarly oblivious to the internals of Simdist.

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Notes

  1. Interestingly, this is also mentioned in the preface to the 1992 edition of [15].

  2. Thus far, Simdist has successfully been used with C++, Lisp, Python, Matlab, bash shell scripts, and the Breve physics simulator.

  3. The parallelization features of the software packages described here are generally poorly documented, and most of the descriptions here are inferences drawn from studying the available source code of the respective software packages. Misinterpretations may have occurred.

  4. Technically, the slave also multiplies the wait time with the job grouping size, as described in the next section.

  5. Simdist = Simulation Distributor; “simdist” on the command line. Note that the slaves are not SIMD, but rather their derivative [17] SPMD [8].

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  1. Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Boye Annfelt Høverstad

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Høverstad, B.A. Simdist: a distribution system for easy parallelization of evolutionary computation. Genet Program Evolvable Mach 11, 185–203 (2010). https://doi.org/10.1007/s10710-009-9100-7

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