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Efficient GPU Implementation of Lucas-Kanade through OpenACC

Abstract : Optical flow estimation stands as an essential component for motion detection and object tracking procedures. It is an image processing algorithm, which is typically composed of a series of convolution masks (approximation of the derivatives) followed by 2 × 2 linear systems for the optical flow vectors. Since we are dealing with a stencil computation for each stage of the algorithm, the overhead from memory accesses is expected to be significant and to yield a genuine scalability bottleneck, especially with the complexity of GPU memory configuration. In this paper, we investigate a GPU deployment of an optimized CPU implementation via OpenACC, a directive-based parallel programming model and framework that ease the process of porting codes to a wide-variety of heterogeneous HPC hardware platforms and architectures. We explore each of the major technical features and strive to get the best performance impact. Experimental results on a Quadro P5000 are provided together with the corres ponding technical discussions, taking the performance of the multicore version on a INTEL Broadwell EP as the baselin
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Contributor : Claire Medrala <>
Submitted on : Wednesday, April 17, 2019 - 10:34:52 AM
Last modification on : Friday, October 23, 2020 - 8:22:02 PM

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Olfa Haggui, Claude Tadonki, Fatma Sayadi, Bouraoui Ouni. Efficient GPU Implementation of Lucas-Kanade through OpenACC. 14th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2019), Feb 2019, Prague, Czech Republic. pp.768-775, ⟨10.5220/0007272107680775⟩. ⟨hal-02102229⟩



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