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Tuning framework for stencil computation in heterogeneous parallel platforms

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Abstract

Image processing and computer vision applications are usually complex in terms of the large amount of processed data and high computation loads. To cope with this, optimization techniques and high-performance hardware platforms are required. Since these applications present many opportunities for parallelism, heterogeneous parallel platforms (HPPs) are an interesting choice, offering a good balance between high computation capabilities and flexibility to handle a large spectrum of application features. Applications such as image filtering and edge detection make extensive use of finite difference method to solve partial derivative equations, which computational pattern is called stencil computation. Stencil computations are known as memory-bound, so that reducing high-latency memory access becomes the biggest challenge to reach high performance. In this paper, we present our methodology as a basis of a performance tuning framework to optimize the implementation of multiple stencil computations on HPPs. Results show that our approach outperforms SDK-based methodologies, improving performance. Moreover, using the proposed approach, the developer has the ability of investigating efficiently the performance of the stencil computations before implementing actual code on the target platforms.

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Notes

  1. A kernel is a function that runs on a GPU. One kernel is executed at a time and many threads execute each kernel.

  2. Occupancy rate is defined as the ratio of the number of allocated threads by the limit allowed by each streaming multiprocessor (SM).

  3. Peripheral component interconnect express.

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Authors and Affiliations

  1. Ecole Polytechnique de Montréal, Montréal, Canada

    Taieb Lamine Ben Cheikh, Alexandra Aguiar & Gabriela Nicolescu

  2. Concordia University, Montréal, Canada

    Sofiene Tahar

Authors
  1. Taieb Lamine Ben Cheikh
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  2. Alexandra Aguiar
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  3. Sofiene Tahar
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  4. Gabriela Nicolescu
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Correspondence to Gabriela Nicolescu.

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Cheikh, T.L.B., Aguiar, A., Tahar, S. et al. Tuning framework for stencil computation in heterogeneous parallel platforms. J Supercomput 72, 468–502 (2016). https://doi.org/10.1007/s11227-015-1575-9

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  • DOI: https://doi.org/10.1007/s11227-015-1575-9

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