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|Title:||Abstract : mapping streaming applications onto GPU systems||Authors:||Huynh, Huynh Phung.
Goh, Rick Siow Mong.
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2012||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
|Appears in Collections:||SCSE Conference Papers|
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