• DocumentCode
    643191
  • Title

    Hardware models for automated partitioning and mapping in multi-core systems

  • Author

    Krawczyk, Lukas ; Kamsties, Erik

  • Author_Institution
    Univ. of Appl. Sci. Fachhochschule, Dortmund, Germany
  • Volume
    02
  • fYear
    2013
  • fDate
    12-14 Sept. 2013
  • Firstpage
    721
  • Lastpage
    725
  • Abstract
    Multi-core CPUs offer several major benefits in mobile and embedded systems. For instance, they provide better energy efficiency and more computing power at the same clock speed compared to single-core CPUs. These benefits do not come for free: A program has to be divided into tasks, which can be executed in parallel on different cores. Partitioning of software and mapping on cores are nontrivial activities that require detailed knowledge about the multi-core CPU, e.g., if shared memories are available, their size, and other properties. Such programmers´ information about a CPU is typically stored in processor handbooks. If this information would be available in a machine readable model, we call it hardware model, the partitioning and mapping activities can be automated. In this paper, we propose a hardware model and illustrate it using an example of a Freescale multi-core CPU. We then discuss a small case study, which illustrates the use of the hardware model in partitioning, mapping, and code generation.
  • Keywords
    clocks; embedded systems; energy conservation; multiprocessing systems; parallel processing; power aware computing; program compilers; shared memory systems; automated mapping activity; automated partitioning activity; clock speed; code generation; computing power; embedded systems; energy efficiency; freescale multicore CPU system; hardware models; machine readable model; mobile systems; shared memories; software partitioning; Data models; Embedded systems; Hardware; Multicore processing; System-on-chip; Unified modeling language; AUTOSAR; embedded systems development; hardware model; model-driven development; multi-core; target mapping;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2013 IEEE 7th International Conference on
  • Conference_Location
    Berlin
  • Print_ISBN
    978-1-4799-1426-5
  • Type

    conf

  • DOI
    10.1109/IDAACS.2013.6663019
  • Filename
    6663019