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https://hdl.handle.net/10356/84219
Title: | Abstract : mapping streaming applications onto GPU systems | Authors: | Huynh, Huynh Phung. Wong, Weng-Fai. Ray, A. Goh, Rick Siow Mong. Hagiescu, Andrei. |
Keywords: | DRNTU::Engineering::Computer science and engineering | Issue Date: | 2012 | Conference: | SC Companion: High Performance Computing, Networking, Storage and Analysis (2012 : Salt Lake City, Utah, United States) | Abstract: | We 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. | URI: | https://hdl.handle.net/10356/84219 http://hdl.handle.net/10220/13114 |
DOI: | 10.1109/SC.Companion.2012.279 | Schools: | School of Computer Engineering | Research Centres: | Parallel and Distributed Computing Centre | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SCSE Conference Papers |
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