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Optimizing Memory Usage and Accesses on CUDA-Based Recurrent Pattern Matching Image Compression

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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorDomingues, Patrício
dc.contributor.authorSilva, João
dc.contributor.authorRibeiro, Tiago
dc.contributor.authorRodrigues, Nuno M. M.
dc.contributor.authorCarvalho, Murilo B. de
dc.contributor.authorFaria, Sérgio M. M.
dc.date.accessioned2026-07-15T11:20:50Z
dc.date.available2026-07-15T11:20:50Z
dc.date.issued2014
dc.descriptionComputational Science and Its Applications - ICCSA 2014 14th International Conference, Guimarães, Portugal, June 30 - July 3, 204, Proceedings, Part IV.
dc.description.abstractThis paper reports the adaptation of the Multidimensional Multiscale Parser (MMP) algorithm to CUDA. Specifically, we focus on memory optimization issues, such as the layout of data structures in memory, the type of GPU memory - shared, constant and global - and on achieving coalesced accesses. MMP is a demanding lossy compression algorithm for images. For example, MMP requires nearly 9000 seconds to encode the 512 x 512 Lenna image on a 2013's Intel Xeon. One of the main challenges to adapt MMP to manycore is related to the dependency over a pattern codebook which is built during the execution. This forces the input image to be processed sequentially. Nonetheless, CUDA-MMP achieves a 12x speedup over the sequential version when ran on an NVIDIA GTX 680. By further optimizing memory operations, the speedup is pushed to 17.1x.eng
dc.description.sponsorshipSponsors Associacao Portuguesa de Investigacao Operacional Kyushu Sangyo University (KSU) Monash University Universidade do Minho University of Basilicata University of Perugia
dc.identifier.citationDomingues, P., Silva, J., Ribeiro, T., Rodrigues, N. M. M., De Carvalho, M. B., & De Faria, S. M. M. (2014). Optimizing Memory Usage and Accesses on CUDA-Based Recurrent Pattern Matching Image Compression. In Computational Science and Its Applications – ICCSA 2014, LNCS vol. 8582, 560-575. Springer, Cham. https://doi.org/10.1007/978-3-319-09147-1_41
dc.identifier.doi10.1007/978-3-319-09147-1_41
dc.identifier.isbn9783319091464
dc.identifier.isbn9783319091471
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10400.8/16594
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-319-09147-1_41
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofComputational Science and Its Applications – ICCSA 2014
dc.relation.ispartofseriesLecture Notes in Computer Science (LNCS)
dc.rights.uriN/A
dc.subjectCUDA
dc.subjectimage compression
dc.subjectmanycore computing
dc.subjectmemory optimization
dc.titleOptimizing Memory Usage and Accesses on CUDA-Based Recurrent Pattern Matching Image Compressioneng
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage575
oaire.citation.startPage560
oaire.citation.titleComputational Science and Its Applications - ICCSA 2014
oaire.citation.volume8582
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameDomingues
person.familyNameM. M. Rodrigues
person.familyNameFaria
person.givenNamePatrício
person.givenNameNuno
person.givenNameSergio
person.identifier.ciencia-idAA15-6185-C477
person.identifier.ciencia-id6917-B121-4E34
person.identifier.ciencia-id8815-4101-28DD
person.identifier.orcid0000-0002-6207-6292
person.identifier.orcid0000-0001-9536-1017
person.identifier.orcid0000-0002-0993-9124
person.identifier.ridC-5245-2011
person.identifier.scopus-author-id13411315400
person.identifier.scopus-author-id7006052345
person.identifier.scopus-author-id14027853900
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relation.isAuthorOfPublicationf69bd4d6-a6ef-4d20-8148-575478909661
relation.isAuthorOfPublication.latestForDiscoveryb88ada5f-0d8b-4e55-ab0a-62aa82ea1388

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