• DocumentCode
    2440921
  • Title

    A simple thermal model for multi-core processors and its application to slack allocation

  • Author

    Wang, Zhe ; Ranka, Sanjay

  • Author_Institution
    Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
  • fYear
    2010
  • fDate
    19-23 April 2010
  • Firstpage
    1
  • Lastpage
    11
  • Abstract
    Power density and heat density of multicore processor system are increasing exponentially with Moore´s Law. High temperature on chip greatly affects its reliability, and the cost of packaging and cooling system increases exponentially with power consumption. For a multicore processor, the peak temperature of a block depends on its own power density as well as power density of other blocks on chip. In this paper, we have developed a simple thermal model, called Matrix Model (MM), that can be used to derive temperature profiles for all the cores of a multicore processor. We theoretically demonstrate the correctness and efficiency of MM. Our simulation results show that the model is comparable to the HotSpot Model for predicting the peak temperature. Besides having lower computational cost, the MM is succinct (a single matrix) and can be used to derive algorithms for a variety of scenarios. We use this model to develop a novel slack allocation algorithm for a workflow represented by Directed Acyclic Graph on a multicore processor.
  • Keywords
    matrix algebra; multiprocessing systems; power aware computing; resource allocation; Moores Law; directed acyclic graph; heat density; hotspot model; matrix model; multicore processor system; power density; slack allocation algorithm; temperature profiles; thermal model; Cooling; Costs; Moore´s Law; Multicore processing; Packaging; Power system modeling; Power system reliability; Predictive models; System-on-a-chip; Temperature; DVS; Multi-core Processor; Slack Allocation; Thermal Model; Thermal-aware Scheduling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1530-2075
  • Print_ISBN
    978-1-4244-6442-5
  • Type

    conf

  • DOI
    10.1109/IPDPS.2010.5470426
  • Filename
    5470426