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dc.contributor.authorAndión, José M.
dc.contributor.authorArenaz Silva, Manuel
dc.contributor.authorBodin, François
dc.contributor.authorRodríguez, Gabriel
dc.contributor.authorTouriño, Juan
dc.date.accessioned2018-07-11T17:10:02Z
dc.date.available2018-07-11T17:10:02Z
dc.date.issued2016-06
dc.identifier.citationAndión, J.M., Arenaz, M., Bodin, F. et al. Int J Parallel Prog (2016) 44: 620. https://doi.org/10.1007/s10766-015-0362-9es_ES
dc.identifier.issn0885-7458
dc.identifier.issn1573-7640
dc.identifier.urihttp://hdl.handle.net/2183/20902
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in International Journal of Parallel Programming. The final authenticated version is available online at: https://doi.org/10.1007/s10766-015-0362-9es_ES
dc.description.abstract[Abstract] The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing systems. Unfortunately, this heterogeneity is also exposed to the programmer complicating its exploitation. This paper presents a new technique to automatically rewrite sequential programs into a parallel counterpart targeting GPU-based heterogeneous systems. The original source code is analyzed through domain-independent computational kernels, which hide the complexity of the implementation details by presenting a non-statement-based, high-level, hierarchical representation of the application. Next, a locality-aware technique based on standard compiler transformations is applied to the original code through OpenHMPP directives. Two representative case studies from scientific applications have been selected: the three-dimensional discrete convolution and the simple-precision general matrix multiplication. The effectiveness of our technique is corroborated by a performance evaluation on NVIDIA GPUs.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; TIN2010-16735es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; TIN2013-42148-Pes_ES
dc.description.sponsorshipGalicia, Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013-055es_ES
dc.description.sponsorshipMinisterio de Educación; AP2008-01012es_ES
dc.language.isoenges_ES
dc.publisherSpringer New York LLCes_ES
dc.relation.urihttps://doi.org/10.1007/s10766-015-0362-9es_ES
dc.subjectHeterogeneous systemses_ES
dc.subjectGPGPUes_ES
dc.subjectLocalityes_ES
dc.subjectAutomatic parallelizationes_ES
dc.subjectOpenHMPPes_ES
dc.subjectDomain-independent kerneles_ES
dc.titleLocality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directiveses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInternational Journal of Parallel Programminges_ES
UDC.volume44es_ES
UDC.issue3es_ES
UDC.startPage620es_ES
UDC.endPage643es_ES
dc.identifier.doi10.1007/s10766-015-0362-9


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