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Evaluation of an OpenMP Parallelization of Lucas-Kanade on a NUMA-Manycore

Abstract : Lucas-Kanade algorithm is a well-known optical flow estimator widely used in image processing for motion detection and object tracking. As a typical image processing algorithm, the procedure is a series of convolution masks followed by 22 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 stand as a serious scalability bottleneck, especially on a NUMA manycore configuration. The objective of this study is therefore to investigate an OpenMP parallelization of Lucas-kanade algorithm on a NUMA manycore, including the performance impact of NUMA-aware settings at runtime. Experimental results on a dual-socket INTEL Broadwell-E/EP is provided together with the corresponding technical discussions
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Submitted on : Wednesday, August 22, 2018 - 2:45:48 PM
Last modification on : Wednesday, November 17, 2021 - 12:33:04 PM


  • HAL Id : hal-01859701, version 1


Olfa Haggui, Claude Tadonki, Fatma Sayadi, Ouni Bouraoui. Evaluation of an OpenMP Parallelization of Lucas-Kanade on a NUMA-Manycore. 9th Workshop on Applications for Multi-Core Architectures (WAMCA 2018), Sep 2018, Lyon, France. ⟨hal-01859701⟩



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