Accelerating CUDA Graph Algorithms at Maximum Warp

Graphs are powerful data representations favored in many computational domains. Modern GPUs have recently shown promising results in accelerating computationally challenging graph problems but their performance suffers heavily when the graph structure is highly irregular, as most real-world graphs tend to be. In this paper, the authors first observe that the poor performance is caused by work imbalance and is an artifact of a discrepancy between the GPU programming model and the underlying GPU architecture. They then propose a novel virtual warp-centric programming method that exposes the traits of underlying GPU architectures to users.

Provided by: Association for Computing Machinery Topic: Big Data Date Added: Feb 2011 Format: PDF

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