Single core SIMD parallelization of GMM background subtraction algorithm for vehicles detection
| Autor: | Dominique Houzet, Lhoussein Mabrouk, Yahya Zennayi, Abdelkrim Hamzaoui, Said Belkouch, Sylvain Huet |
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| Přispěvatelé: | GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université Cadi Ayyad [Marrakech] (UCA), MAScIR Foundation, ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Huet, Sylvain, Laboratoires d'excellence - Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique - - PERSYVAL-lab2011 - ANR-11-LABX-0025 - LABX - VALID |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]
[INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR] Background subtraction Speedup Computer science 020207 software engineering 02 engineering and technology Parallel computing Object detection [INFO.INFO-ES] Computer Science [cs]/Embedded Systems Instruction set Vectorization (mathematics) [INFO.INFO-DC] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Single-core [INFO.INFO-ES]Computer Science [cs]/Embedded Systems SSE2 SIMD [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] Algorithm ComputingMilieux_MISCELLANEOUS |
| Zdroj: | CiSt 2018-IEEE International Colloquium on Information Science and Technology CiSt 2018-IEEE International Colloquium on Information Science and Technology, Oct 2018, Marrakech, Morocco CIST |
| Popis: | Gaussian Mixture Model background subtraction (GMM) method is nowadays used in many moving object detection applications. This pixel wise approach is performed by applying the same arithmetic operations on independent data elements. Thus, the vector execution of these operations is an efficient acceleration method; especially because most of modern SIMD architectures implement long registers in their processors. In this paper, we propose an efficient data vectorization of GMM on two different Intel architectures using the SSE2 vector instructions. This is done by parallelizing the hotspots that consume big processing time in the algorithm, as well as using suitable compilation environment. The performance evaluation for varying datasets using only one core showed that a speedup of 1,8 is achieved. This is satisfactory for this algorithm where many portions are sequential, and where most of computation is done in single-precision floating-point. Hence, this has limited the number of elements that we could pack in the core registers. |
| Databáze: | OpenAIRE |
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