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Is GPGPU CCL worth it?: A performance comparison between some GPU and CPU algorithms for solving connected components labeling on binary images
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Lönar sig GPGPU CCL? (Swedish)
Abstract [en]

Connectedcomponentlabeling(CCL)isatraditionallysequentialproblem that is hard to parallelize. This report aims to test the performance of solving CCL using massively parallel hardware through GPGPU. To achieve this several CCL algorithms were researched and implemented using C++ and OpenCL. The results showed an improvement of up to a factor of 2, which is insignificant when also considering memory transfer. In conclusion, performing CCL on the GPU is not worth it if the data has to first be transferred to and from the GPU.

Abstract [sv]

Etikettering av sammansatta komponenter (CCL) är ett traditionellt sekventiellt problem som är svårt att parallellisera. Denna rapport ämnar atttestaprestandanavattlösaCCLmedanvändningavmassivtparallell hårdvara genom metoden GPGPU. För att uppnå detta undersöktes och implementerades ett flertal CCL algoritmer i C++ och OpenCL Resultaten pekar på en förbättring upp till en faktor av 2, vilket är obetydligt när man också tar hänsyn till minnesöverföringen. Sammanfattningsvis så är det ej värt att utföra CCL med GPGPU om data även måste överföras till och från GPU.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-186270OAI: oai:DiVA.org:kth-186270DiVA: diva2:926622
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Available from: 2016-05-18 Created: 2016-05-09 Last updated: 2016-05-18Bibliographically approved

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CiteExportLink to record
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  • apa
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