IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Self-optimizing Concurrency in Software Transactional Memory via Model-based Approach

  • Pierangelo Di Sanzo

    ()

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Francesco Del Re

    ()

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Diego Rughetti

    ()

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Bruno Ciciani

    ()

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Francesco Quaglia

    ()

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

Registered author(s):

    In the era of multi-core systems, the need for tools simplifying the development of concurrent applications is increasingly looming. In such a context, Software Transactional Memory (STM) is recognized as an effective programming paradigm, thanks to its ability to guarantee consistency of data that are shared across concurrent threads in an application transparent manner. On the other hand, a core problem to cope with for STM, which has received great attention of late, deals with (dynamically) regulating the degree of concurrency, in order to deliver optimal performance. In fact, depending on the application workload, whose profile can also change over time, an oversized number of concurrent threads may cause loss in performance due to excessive data contention, which may give rise to excessively high transaction abort rate. Conversely, an undersized number of threads may hamper performance due to limited exploitation of parallelism. We address this problem by proposing a self-regulation approach of the concurrency level, which leverages a parametric analytical performance model aimed at predicting the scalability of the STM application as a function of the actual workload profile and the number of concurrent threads supposed to sustaining the execution. The regulation scheme allows achieving optimal performance during the whole lifetime of the application via dynamically resizing the number of concurrent threads according to the predictions by the model. The later is customized for a specific application/platform pair through regression analysis, which is based on a lightweight sampling phase. We also present a real implementation of the model-based concurrency self-regulation architecture integrated within the open source TinySTM framework. Further, the effectiveness of the proposal is evaluated via an experimental assessment based on standard STM benchmark applications.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://www.dis.uniroma1.it/~bibdis/RePEc/aeg/report/2013-07.pdf
    File Function: Revised version, 2013
    Download Restriction: no

    Paper provided by Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza" in its series DIAG Technical Reports with number 2013-07.

    as
    in new window

    Length: 24 pages
    Date of creation: Jul 2013
    Date of revision:
    Handle: RePEc:aeg:report:2013-07
    Contact details of provider: Phone: +390677274140
    Fax: +39 0677274129
    Web page: http://www.dis.uniroma1.it
    Email:


    More information through EDIRC

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:aeg:report:2013-07. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Antonietta Angelica Zucconi)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.