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Measurement-based e...
Measurement-based evaluation of data-parallelism for OpenCV feature-detection algorithms
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- Danielsson, Jakob (författare)
- Mälardalens högskola,Inbyggda system
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- Marcus, Jägemar, 1972- (författare)
- Mälardalens högskola,Inbyggda system,Ericsson AB, Stockholm, Sweden
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- Behnam, Moris, 1973- (författare)
- Mälardalens högskola,Inbyggda system
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- Sjödin, Mikael (författare)
- Mälardalens högskola,Inbyggda system
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- Seceleanu, Tiberiu (författare)
- Mälardalens högskola,Inbyggda system
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(creator_code:org_t)
- 2018
- 2018
- Engelska.
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Ingår i: Staying Smarter in a Smartening World COMPSAC'18. - 9781538626665 ; , s. 701-710
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Multi-core
- OpenCV
- Cache
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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