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Steal Locally, Share Globally

A Strategy for Multiprogramming in the Manycore Era

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Abstract

In a general-purpose computing system, several parallel applications run simultaneously on the same platform. Even if each application is highly tuned for that specific platform, additional performance issues are arising in such a dynamic environment in which multiple applications compete for the resources. Different scheduling and resource management techniques have been proposed either at operating system or user level to improve the performance of concurrent workloads. In this paper, we propose a task-based strategy called “Steal Locally, Share Globally” implemented in the runtime of our parallel programming model GPRM (Glasgow Parallel Reduction Machine). We have chosen a state-of-the-art manycore parallel machine, the Intel Xeon Phi, to compare GPRM with some well-known parallel programming models, OpenMP, Intel Cilk Plus and Intel TBB, in both single-programming and multiprogramming scenarios. We show that GPRM not only performs well for single workloads, but also outperforms the other models for multiprogramming workloads. There are three considerations regarding our task-based scheme: (i) It is implemented inside the parallel framework, not as a separate layer; (ii) It improves the performance without the need to change the number of threads for each application (iii) It can be further tuned and improved, not only for the GPRM applications, but for other equivalent parallel programming models.

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Notes

  1. TCB is called “subtask list” in the previous GPRM papers.

  2. These features can be enabled via command-line switches when compiling the GPRM runtime system. It that sense they can be considered as runtime support features.

  3. We use the noun “steal” (OED “the act of stealing”) rather than “theft”.

  4. The “sequential tile” is responsible for the sequential tasks, but also contributes to the parallel execution, whenever required.

  5. For all experiments, results from the benchmarks kernel are considered in the figures (a) and (b), while in the other results taken from the VTune Amplifier, all information from the start of the application, including its initial phase and the CPU time consumed by the shared libraries is taken into account.

  6. The sequential phase of the MergeSort benchmark with the input size 80M is around 2 s, and the initial phase of the MatMul benchmark with the input size 4096\(\times \)4096 is about half a second.

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  1. School of Computing Science, University of Glasgow, Glasgow, UK

    Ashkan Tousimojarad & Wim Vanderbauwhede

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  1. Ashkan Tousimojarad
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  2. Wim Vanderbauwhede
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Correspondence to Ashkan Tousimojarad.

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Tousimojarad, A., Vanderbauwhede, W. Steal Locally, Share Globally. Int J Parallel Prog 43, 894–917 (2015). https://doi.org/10.1007/s10766-015-0350-0

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