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Article dans une revue

Harris corner detection on a NUMA manycore

Abstract : Corner detection is a key kernel for many image processing procedures including pattern recognition and motion detection. The latter, for instance, mainly relies on the corner points for which spatial analyses are performed, typically on (probably live) videos or temporal flows of images. Thus, highly efficient corner detection is essential to meet the real-time requirement of associated applications. In this paper, we consider the corner detection algorithm proposed by Harris, whose the main work-flow is a composition of basic operators represented by their approximations using 3 3 matrices. The corresponding data access patterns follow a stencil model, which is known to require careful memory organization and management. Cache misses and other additional hindering factors with NUMA architectures need to be skillfully addressed in order to reach an efficient scalable implementation. In addition, with an increasingly wide vector registers, an efficient SIMD version should be designed and explicitly implemented. In this paper, we study a direct and explicit implementation of common and novel optimization strategies, and provide a NUMA-aware parallelization. Experimental results on a dual-socket INTEL Broadwell-E/EP show a noticeably good scalability performance.
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Contributeur : Claire Medrala <>
Soumis le : vendredi 16 février 2018 - 16:34:10
Dernière modification le : jeudi 24 septembre 2020 - 16:36:04

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Olfa Haggui, Claude Tadonki, Lionel Lacassagne, Fatma Sayadi, Ouni Bouraoui. Harris corner detection on a NUMA manycore. Future Generation Computer Systems, Elsevier, 2018, 88, pp.442-452. ⟨10.1016/j.future.2018.01.048⟩. ⟨hal-01711050⟩



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