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Measurement-based evaluation of data-parallelism for OpenCV feature-detection algorithms
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Ericsson AB, Stockholm, Sweden.ORCID iD: 0000-0003-2612-4135
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1687-930X
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-7586-0409
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2018 (English)In: Staying Smarter in a Smartening World COMPSAC'18, 2018, p. 701-710Conference paper, Published paper (Refereed)
Abstract [en]

We investigate the effects on the execution time, shared cache usage and speed-up gains when using data-partitioned parallelism for the feature detection algorithms available in the OpenCV library. We use a data set of three different images which are scaled to six different sizes to exercise the different cache memories of our test architectures. Our measurements reveal that the algorithms using the default settings of OpenCV behave very differently when using data-partitioned parallelism. Our investigation shows that the executions of the algorithms SURF, Dense and MSER correlate to L3-cache usage and they are therefore not suitable for data-partitioned parallelism on multi-core CPUs. Other algorithms: BRISK, FAST, ORB, HARRIS, GFTT, SimpleBlob and SIFT, do not correlate to L3-cache in the same extent, and they are therefore more suitable for data-partitioned parallelism. Furthermore, the SIFT algorithm provides the most stable speed-up, resulting in an execution between 3 and 3.5 times faster than the original execution time for all image sizes. We also have evaluated the hardware resource usage by measuring the algorithm execution time simultaneously with the L3-cache usage. We have used our measurements to conclude which algorithms are suitable for parallelization on hardware with shared resources.

Place, publisher, year, edition, pages
2018. p. 701-710
Keywords [en]
Multi-core, OpenCV, Cache
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-40855DOI: 10.1109/COMPSAC.2018.00105Scopus ID: 2-s2.0-85055434865ISBN: 9781538626665 (print)OAI: oai:DiVA.org:mdh-40855DiVA, id: diva2:1249860
Conference
42nd IEEE Computer Software and Applications Conference, COMPSAC 2018; Tokyo; Japan; 23 July 2018 through 27 July 2018
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlAvailable from: 2018-09-20 Created: 2018-09-20 Last updated: 2019-01-04Bibliographically approved

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Danielsson, JakobMarcus, JägemarBehnam, MorisSeceleanu, Tiberiu

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