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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: 2019-11-11Bibliographically approved
In thesis
1. Characterization of Shared Resource Contention in Multi-core Systems
Open this publication in new window or tab >>Characterization of Shared Resource Contention in Multi-core Systems
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Multi-core computers are infamous for being hard to use in time-critical systems due to execution-time variations as an effect of shared resource contention. In this thesis we study the problem of shared resource contention which occurs when multiple applications executing on different cores do not have exclusive ownership of a shared resource. We investigate performance variations of parallel tasks in multi-core systems and present a method to pinpoint the source of the resource contention using existing hardware performance counters. Furthermore, we investigate methods to mitigate performance variations using resource isolation techniques. We present a methodology for verifying isolation and tested the achieved isolation using the Jailhouse hypervisor. We further investigate shared cache memory isolation techniques using a page coloring tool called PALLOC. Page-coloring is used for partitioning the cache, assigning specific cache lines to specific processes. Page coloring can however cause system performance degradation since it decreases the total amount of cache memory available for each process. Finally, we propose a dynamic partitioning assignment policy which assigns cache partitions to a process according to an adaptive model based on the process performance. The general conclusion from our investigations is that a large body of applications can suffer from shared resource contention and that techniques for mitigating resource contention are in dire need. Our methods measure and characterise applications, identifies resource contention and finally study isolation techniques.  

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019. p. 160
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 287
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-45932 (URN)978-91-7485-449-7 (ISBN)
Presentation
2019-12-17, Paros, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-18Bibliographically approved

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Danielsson, JakobMarcus, JägemarBehnam, MorisSeceleanu, Tiberiu

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