Garaph: Efficient GPU-accelerated Graph Processing on a Single Machine with Balanced Replication


Lingxiao Ma, Zhi Yang, and Han Chen, Computer Science Department, Peking University, Beijing, China; Jilong Xue, Microsoft Research, Beijing, China; Yafei Dai, Institute of Big Data Technologies, Shenzhen Key Lab for Cloud Computing Technology & Applications, School of Electronics and Computer Engineering (SECE), Peking University, Shenzhen, China


Recent advances in storage (e.g., DDR4, SSD, NVM) and accelerators (e.g., GPU, Xeon-Phi, FPGA) provide the opportunity to efficiently process large-scale graphs on a single machine. In this paper, we present Garaph, a GPU-accelerated graph processing system on a single machine with secondary storage as memory extension. Garaph is novel in three ways. First, Garaph proposes a vertex replication degree customization scheme that maximizes the GPU utilization given vertices’ degrees and space constraints. Second, Garaph adopts a balanced edge-based partition ensuring work balance over CPU threads, and also a hybrid of notify-pull and pull computation models optimized for fast graph processing on the CPU. Third, Garaph uses a dynamic workload assignment scheme which takes into account both characteristics of processing elements and graph algorithms. Our evaluation with six widely used graph applications on seven real-world graphs shows that Garaph significantly outperforms existing state-of-art CPU-based and GPU-based graph processing systems, getting up to 5.36x speedup over the fastest among them.

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@inproceedings {203215,
author = {Lingxiao Ma and Zhi Yang and Han Chen and Jilong Xue and Yafei Dai},
title = {Garaph: Efficient {GPU-accelerated} Graph Processing on a Single Machine with Balanced Replication},
booktitle = {2017 USENIX Annual Technical Conference (USENIX ATC 17)},
year = {2017},
isbn = {978-1-931971-38-6},
address = {Santa Clara, CA},
pages = {195--207},
url = {},
publisher = {USENIX Association},
month = jul

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