Multilayer Cache Partitioning for Multiprogram Workloads

  • Mahmut Kandemir
  • Ramya Prabhakar
  • Mustafa Karakoy
  • Yuanrui Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6852)


We present a fully-automated, model based, multilayer cache partitioning scheme for multiprogram workloads running on multicore machines. As opposed to prior efforts, this scheme partitions shared caches at multiple layers simultaneously in a coordinated fashion. This scheme tries to achieve two objectives. First, it tries to satisfy the specified quality of service (QoS) values for all applications by partitioning the shared cache hierarchy across them, and second, it distributes the remaining excess cache capacity (if any) across applications such that a global performance metric is maximized. Our experimental analysis shows that the proposed multilayer partitioning scheme generates, on average, 33.1% improvement (on the weighted speedup metric) over the next best-performing scheme and is very successful in satisfying the QoS requirements of applications. Also, we show that partitioning each layer in isolation cannot generate the benefits obtained through our coordinated partitioning scheme. In addition, we observed that the difference between our scheme and an optimal scheme (that derives best dynamic partitions) was less than 15% for all the workloads tested and 6.6% on average.


Multicore Architecture Cache Space Cache Hierarchy Cache Partitioning Weighted Speedup 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mahmut Kandemir
    • 1
  • Ramya Prabhakar
    • 1
  • Mustafa Karakoy
    • 2
  • Yuanrui Zhang
    • 1
  1. 1.Pennsylvania State UniversityUSA
  2. 2.Imperial CollegeUK

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