Chanyoung Oh
Xipeng Shen
Jidong Zhai
ABSTRACT
Recent studies have shown promising performance benefits when multiple stages of a pipelined stencil application are mapped to different parts of a GPU to run concurrently. An important factor for the computing efficiency of such pipelines is the granularity of a task. In previous programming frameworks that support true pipelined computations on GPU, the choice has to be made by the programmers during the application development time. Due to many difficulties, programmers' decisions are often far from optimal, causing inferior performance and performance portability.
This paper presents GOPipe, a granularity-oblivious programming framework for efficient pipelined stencil executions on GPU. With GOPipe, programmers no longer need to specify the appropriate task granularity. GOPipe automatically finds it, and dynamically schedules tasks of that granularity for efficiency while observing all inter-task and inter-stage data dependencies. In our experiments on six real-life applications and various scenarios, GOPipe outperforms the state-of-the-art system by 1.39X on average with a much better programming productivity.
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Index Terms
- GOPipe: A Granularity-Oblivious Programming Framework for Pipelined Stencil Executions on GPU
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