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Efficient Parallel Graph Exploration on Multi-Core CPU and GPU

Sungpack Hong, Tayo Oguntebi, Kunle Olukotun
Pervasive Parallelism Laboratory, Stanford University
International Conference on Parallel Architectures and Compilation Techniques (PACT), 2011

@article{hong2011efficient,

   title={Efficient Parallel Graph Exploration for Multi-Core CPU and GPU},

   author={Hong, S. and Oguntebi, T. and Olukotun, K.},

   journal={Parallel Architectures and Compilation Techniques, New York, NY, USA},

   year={2011}

}

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Graphs are a fundamental data representation that has been used extensively in various domains. In graph-based applications, a systematic exploration of the graph such as a breadth-first search (BFS) often serves as a key component in the processing of their massive data sets. In this paper, we present a new method for implementing the parallel BFS algorithm on multi-core CPUs which exploits a fundamental property of randomly shaped real-world graph instances. By utilizing memory bandwidth more efficiently, our method shows improved performance over the current state-of-the-art implementation and increases its advantage as the size of the graph increases. We then propose a hybrid method which, for each level of the BFS algorithm, dynamically chooses the best implementation from: a sequential execution, two different methods of multicore execution, and a GPU execution. Such a hybrid approach provides the best performance for each graph size while avoiding poor worst-case performance on high-diameter graphs. Finally, we study the effects of the underlying architecture on BFS performance by comparing multiple CPU and GPU systems, a high-end GPU system performed as well as a quad-socket high-end CPU system.
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