Preliminary analysis of feasible benchmark problems for the hydrid PRAM/NUMA REPLICA architecture

J.-M. Mäkelä, V. Leppänen, Martti Forsell

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    Abstract

    We study benchmarking on modern chip multi-processors (CMP), and outline a set of programs to measure the architectural performance properties, focusing on the REPLICA architecture employing a hybrid of PRAM and NUMA computational models. We analyse the parallel data processing and storage mechanisms on mainstream and research CMPs and their utilization in benchmarks to identify the strong and weak points of REPLICA and to further develop the benchmarks to demonstrate its scalability and performance
    Original languageEnglish
    Title of host publicationProceedings of the 13th International Conference on Computer Systems and Technologies
    Subtitle of host publicationCompSysTech 2012
    EditorsBoris Rachev, Angel Smrikarov
    Place of PublicationNew York, NY, USA
    PublisherAssociation for Computing Machinery ACM
    Pages37-44
    ISBN (Print)978-1-4503-1193-9
    DOIs
    Publication statusPublished - 2012
    MoE publication typeNot Eligible
    Event13th International Conference on Computer Systems and Technologies, CompSysTech 2012 - Ruse, Bulgaria
    Duration: 22 Jun 201223 Jun 2012

    Conference

    Conference13th International Conference on Computer Systems and Technologies, CompSysTech 2012
    Abbreviated titleCompSysTech 2012
    CountryBulgaria
    CityRuse
    Period22/06/1223/06/12

    Keywords

    • Benchmarking
    • multi-core
    • parallel computing
    • processor architecture

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  • Cite this

    Mäkelä, J-M., Leppänen, V., & Forsell, M. (2012). Preliminary analysis of feasible benchmark problems for the hydrid PRAM/NUMA REPLICA architecture. In B. Rachev, & A. Smrikarov (Eds.), Proceedings of the 13th International Conference on Computer Systems and Technologies: CompSysTech 2012 (pp. 37-44). Association for Computing Machinery ACM. https://doi.org/10.1145/2383276.2383283