SCHEDULE: NOV 10-16, 2012
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Automated Mapping Streaming Applications onto GPUs
SESSION: Research Poster Reception
EVENT TYPE: Posters and Electronic Posters
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Huynh Phung Huynh, Andrei Hagiescu, Weng-Fai Wong, Rick Goh Siow Mong, Abhishek Ray
ROOM:East Entrance
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. 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.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Huynh Phung Huynh - Singapore Agency for Science, Technology and Research
Andrei Hagiescu - National University of Singapore
Weng-Fai Wong - National University of Singapore
Rick Goh Siow Mong - Singapore Agency for Science, Technology and Research
Abhishek Ray - Nanyang Technological University
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Automated Mapping Streaming Applications onto GPUs
SESSION: Research Poster Reception
EVENT TYPE:
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Huynh Phung Huynh, Andrei Hagiescu, Weng-Fai Wong, Rick Goh Siow Mong, Abhishek Ray
ROOM:East Entrance
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. 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.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Huynh Phung Huynh - Singapore Agency for Science, Technology and Research
Andrei Hagiescu - National University of Singapore
Weng-Fai Wong - National University of Singapore
Rick Goh Siow Mong - Singapore Agency for Science, Technology and Research
Abhishek Ray - Nanyang Technological University
Click here to download .ics calendar file