Autonomic scheduling of tasks from data parallel patterns to CPU/GPU core mixes

T. Serban, M. Danelutto, P. Kilpatrick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

We propose a methodology for optimizing the execution of data parallel (sub-)tasks on CPU and GPU cores of the same heterogeneous architecture. The methodology is based on two main components: i) an analytical performance model for scheduling tasks among CPU and GPU cores, such that the global execution time of the overall data parallel pattern is optimized; and ii) an autonomic module which uses the analytical performance model to implement the data parallel computations in a completely autonomic way, requiring no programmer intervention to optimize the computation across CPU and GPU cores. The analytical performance model uses a small set of simple parameters to devise a partitioning-between CPU and GPU cores-of the tasks derived from structured data parallel patterns/algorithmic skeletons. The model takes into account both hardware related and application dependent parameters. It computes the percentage of tasks to be executed on CPU and GPU cores such that both kinds of cores are exploited and performance figures are optimized. The autonomic module, implemented in FastFlow, executes a generic map (reduce) data parallel pattern scheduling part of the tasks to the GPU and part to CPU cores so as to achieve optimal execution time. Experimental results on state-of-the-art CPU/GPU architectures are shown that assess both performance model properties and autonomic module effectiveness.

Original languageEnglish
Title of host publicationProceedings of the 2013 International Conference on High Performance Computing and Simulation, HPCS 2013
Pages72-79
Number of pages8
DOIs
Publication statusPublished - 26 Nov 2013
Event2013 11th International Conference on High Performance Computing and Simulation, HPCS 2013 - Helsinki, Finland
Duration: 01 Jul 201305 Jul 2013

Conference

Conference2013 11th International Conference on High Performance Computing and Simulation, HPCS 2013
CountryFinland
CityHelsinki
Period01/07/201305/07/2013

Keywords

  • autonomic computing
  • data parallelism
  • GPU
  • parallel design patterns

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation

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  • Cite this

    Serban, T., Danelutto, M., & Kilpatrick, P. (2013). Autonomic scheduling of tasks from data parallel patterns to CPU/GPU core mixes. In Proceedings of the 2013 International Conference on High Performance Computing and Simulation, HPCS 2013 (pp. 72-79). [6641395] https://doi.org/10.1109/HPCSim.2013.6641395