Efficient GPU Implementation of Lucas-Kanade through OpenACC

Olfa Haggui, Claude Tadonki, Fatma Sayadi, Bouraoui Ouni

2019

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 corresponding technical discussions, taking the performance of the multicore version on a INTEL Broadwell EP as the baseline.

Paper Citation

in Harvard Style

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, SciTePress, pages 768-775. DOI: 10.5220/0007272107680775

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Haggui O.
AU - Tadonki C.
AU - Sayadi F.
AU - Ouni B.
PY - 2019
SP - 768
EP - 775
DO - 10.5220/0007272107680775
PB - SciTePress