Research Output
Experiments in Multicore and Distributed Parallel Processing using JCSP
  It is currently very difficult to purchase any form of computer system be it, notebook, laptop, desktop server or high performance computing system that does not contain a multicore processor. Yet the designers of applications, in general, have very little experience and knowledge of how to exploit this capability. Recently, the Scottish Informatics and Computer Science Alliance (SICSA) issued a challenge to investigate the ability of developers to parallelise a simple Concordance algorithm. Ongoing work had also shown that the use of multicore processors for applications that have internal parallelism is not as straightforward as might be imagined. Two applications are considered: calculating π using Monte Carlo methods and the SICSA Concordance application. The ease with which parallelism can be extracted from a single application using both single multicore processors and distributed networks of such multicore processors is investigated. It is shown that naïve application of parallel programming techniques does not produce the desired results and that considerable care has to be taken if multicore systems are to result in improved performance. Meanwhile the use of distributed systems tends to produce more predictable and reasonable benefits resulting from parallelisation of applications.

  • Date:

    31 December 2011

  • Publication Status:

    Published

  • DOI:

    10.3233/978-1-60750-774-1-131

  • Library of Congress:

    QA76 Computer software

  • Dewey Decimal Classification:

    005 Computer programming, programs & data

  • Funders:

    Edinburgh Napier Funded

Citation

Kerridge, J. (2011). Experiments in Multicore and Distributed Parallel Processing using JCSP. In J. F. Broenink, F. R. Barnes, J. Kerridge, P. H. Welch, A. T. Sampson, & J. B. Pedersen (Eds.), Communicating Process Architectures 2011, (131-142). https://doi.org/10.3233/978-1-60750-774-1-131

Authors

Editors

Keywords

multicore processors; parallelism; distributed networks

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