loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Olfa Haggui 1 ; Claude Tadonki 2 ; Fatma Sayadi 3 and Bouraoui Ouni 4

Affiliations: 1 Centre de Recherche en Informatique (CRI), Mines ParisTech - PSL Research University, 60 boulevard Saint-Michel, 75006 Paris, France, Networked Objects Control and Communications Systems (NOCCS), Sousse National School of Engineering, BP 264 Sousse Erriadh 4023 and Tunisia ; 2 Centre de Recherche en Informatique (CRI), Mines ParisTech - PSL Research University, 60 boulevard Saint-Michel, 75006 Paris and France ; 3 Electronics and Microelectronics Laboratory,Faculty of Sciences, University of Monastir, 5000 Monastir and Tunisia ; 4 Networked Objects Control and Communications Systems (NOCCS), Sousse National School of Engineering, BP 264 Sousse Erriadh 4023 and Tunisia

Keyword(s): Optical Flow, Lucas-Kanade, Multicore, Manycore, GPU, OpenACC.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Optical Flow and Motion Analyses ; Tracking and Visual Navigation

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 corre sponding technical discussions, taking the performance of the multicore version on a INTEL Broadwell EP as the baseline. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.235.186.149

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Haggui, O.; Tadonki, C.; Sayadi, F. and Ouni, B. (2019). Efficient GPU Implementation of Lucas-Kanade through OpenACC. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 768-775. DOI: 10.5220/0007272107680775

@conference{visapp19,
author={Olfa Haggui. and Claude Tadonki. and Fatma Sayadi. and Bouraoui Ouni.},
title={Efficient GPU Implementation of Lucas-Kanade through OpenACC},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={768-775},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007272107680775},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Efficient GPU Implementation of Lucas-Kanade through OpenACC
SN - 978-989-758-354-4
IS - 2184-4321
AU - Haggui, O.
AU - Tadonki, C.
AU - Sayadi, F.
AU - Ouni, B.
PY - 2019
SP - 768
EP - 775
DO - 10.5220/0007272107680775
PB - SciTePress