Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84219
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dc.contributor.authorHuynh, Huynh Phung.en
dc.contributor.authorWong, Weng-Fai.en
dc.contributor.authorRay, A.en
dc.contributor.authorGoh, Rick Siow Mong.en
dc.contributor.authorHagiescu, Andrei.en
dc.date.accessioned2013-08-15T07:01:21Zen
dc.date.accessioned2019-12-06T15:40:47Z-
dc.date.available2013-08-15T07:01:21Zen
dc.date.available2019-12-06T15:40:47Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.urihttps://hdl.handle.net/10356/84219-
dc.description.abstractWe describe an efficient and scalable code generation framework that automatically maps general purpose streaming applications onto GPU systems. This architecture-driven framework takes into account the idiosyncrasies of the GPU pipeline and the unique memory hierarchy. The framework has been implemented as a back-end to the StreamIt programming language compiler. Several key features in this framework ensure maximized performance and scalability. First, the generated code increases the effectiveness of the on-chip memory hierarchy by employing a heterogeneous mix of compute and memory access threads. Our scheme goes against the conventional wisdom of GPU programming which is to use a large number of homogeneous threads. Second, we utilise an efficient stream graph partitioning algorithm to handle larger applications and achieve the best performance under the given on-chip memory constraints. Lastly, the framework maps complex applications onto multiple GPUs using a highly effective pipeline execution scheme. Our comprehensive experiments show its scalability and significant speedup compared to a state-of-the-art solution.en
dc.language.isoenen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleAbstract : mapping streaming applications onto GPU systemsen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.conferenceSC Companion: High Performance Computing, Networking, Storage and Analysis (2012 : Salt Lake City, Utah, United States)en
dc.contributor.researchParallel and Distributed Computing Centreen
dc.identifier.doi10.1109/SC.Companion.2012.279en
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:SCSE Conference Papers

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