Please use this identifier to cite or link to this item: https://doi.org/10.1109/SC.Companion.2012.279
Title: Mapping streaming applications onto GPU systems
Authors: Huynh, H.P.
Hagiescu, A.
Wong, W.-F. 
Goh, R.S.M.
Ray, A.
Keywords: GPU
Multi-GPU
Streaming Application
StreamIt
Issue Date: 2012
Citation: Huynh, H.P.,Hagiescu, A.,Wong, W.-F.,Goh, R.S.M.,Ray, A. (2012). Mapping streaming applications onto GPU systems. Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012 : 1488-1490. ScholarBank@NUS Repository. https://doi.org/10.1109/SC.Companion.2012.279
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. © 2012 IEEE.
Source Title: Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/78221
ISBN: 9780769549569
DOI: 10.1109/SC.Companion.2012.279
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