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Effective runtime scheduling for high-performance graph processing on heterogeneous dataflow architecture

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Abstract

Graph processing is widely used in modern society, such as social networks, bioinformatics, and information networks. It is observed that the dataflow architecture has been demonstrated to effectively resolve the challenges of low instruction-level parallelism and branch mispredictions in the existing general-purpose architecture for graph applications. In this paper, toward a customized heterogeneous dataflow architecture that integrates the hardware advantages of both dataflow architecture and traditional control architecture, we propose a novel runtime system that can adaptively offload each subgraph to an appropriate underlying architecture. We also present a hybrid execution model to drive optimal performance. Our implementation on a CPU-FPGA platform shows that our approach achieves 2.2x throughput improvement over a state-of-art CPU-FPGA graph processing accelerator and 2.4x throughput improvement over a state-of-art FPGA-based design.

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful comments and valuable feedback. This work is supported by the National Key Research and Development Program of China under Grant No. 2018YFB1003502, National Natural Science Foundation of China under Grant No. 61832006, 61825202, 61702201, and 61929103.

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Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan, 430074, China

    Qingxiang Chen, Long Zheng, Xiaofei Liao, Hai Jin & Qinggang Wang

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  1. Qingxiang Chen
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  2. Long Zheng
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  3. Xiaofei Liao
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  4. Hai Jin
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  5. Qinggang Wang
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Correspondence to Hai Jin.

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Chen, Q., Zheng, L., Liao, X. et al. Effective runtime scheduling for high-performance graph processing on heterogeneous dataflow architecture. CCF Trans. HPC 2, 362–375 (2020). https://doi.org/10.1007/s42514-020-00041-w

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