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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.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.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
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