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A BSP model graph processing system on many cores

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Abstract

Large-scale graph processing plays an increasingly important role for many data-related applications. Recently GPU has been adopted to accelerate various graph processing algorithms. However, since the architecture of GPU is very different from traditional computing model, the learning threshold for developing GPU-based applications is high. In this paper, we propose a GPU-based parallel graph processing system named GPregel to tackle this challenge. GPregel is a BSP model in graph processing such as Pregel from Google. It harnesses a lightweight compiler to hide the underlying complexity of the parallel processing details and simplifies programming, so that it greatly reduces the difficulty in utilizing the GPU to solve graph computing problems. Moreover, GPregel develops several optimizations for enhancing the performance, including (1) a special storage model for BSP model running on GPU, which overcomes the execution divergence and irregular memory access by coarse-grained designs; (2) a warp-level optimal strategy Parallelized-Messages-Sending and a thread-level optimal strategy Threads-Merge-Executing to accelerate the computations of high degree vertexes and low degree vertexes respectively; (3) messages copy mechanism optimization that utilizes a shared array and a rolling array to speed up the messages copy. Experiments demonstrate that GPregel can achieve high performance with little work for developers.

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Acknowledgements

This paper is supported in part by the National Natural Science Foundation of China under Grant No. 61472454.

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Siyan Lai, Guangda Lai, Fangzhou Lu, Guojun Shen, Jing Jin & Xiaola Lin

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  1. Siyan Lai
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  2. Guangda Lai
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Correspondence to Siyan Lai.

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Lai, S., Lai, G., Lu, F. et al. A BSP model graph processing system on many cores. Cluster Comput 20, 1359–1377 (2017). https://doi.org/10.1007/s10586-017-0829-0

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