Name
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 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 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-EIEP is provided together with the corresponding technical discussions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CAHPC.2018.8645936 | 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) |
Keywords | Field | DocType |
Optical flow,Lucas-Kanade,multicore,manycore,openMP NUMA,scalability | Bottleneck,Motion detection,Computer science,Parallel computing,Image processing,Stencil code,Lucas–Kanade method,Multi-core processor,Optical flow,Scalability | Conference |
ISSN | ISBN | Citations |
1550-6533 | 978-1-5386-7769-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olfa Haggui | 1 | 0 | 0.34 |
| Claude Tadonki | 2 | 7 | 2.78 |
| Fatma Ezahra Sayadi | 3 | 17 | 5.98 |
| Bouraoui Ouni | 4 | 43 | 9.42 |