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Accelerating All-Edge Common Neighbor Counting on Three Processors

By Yulin Che, Zhuohang Lai, Shixuan Sun, Prof. Qiong Luo and Yue Wang.

We propose to accelerate an important but time-consuming operation in online graph analytics, which is the counting of common neighbors for each pair of adjacent vertices (u,v), or edge (u,v), on three modern processors of different architectures. We study two representative algorithms for this problem: (1) a merge-based pivot-skip algorithm (MPS) that intersects the two sets of neighbor vertices of each edge (u,v) to obtain the count; and (2) a bitmap-based algorithm (BMP), which dynamically constructs a bitmap index on the neighbor set of each vertex u, and for each neighbor v of u, looks up v’s neighbors in u’s bitmap. We parallelize and optimize both algorithms on a multicore CPU, an Intel Xeon Phi Knights Landing processor (KNL), and an NVIDIA GPU. Our experi- ments show that (1) Both the CPU and the GPU favor BMP whereas MPS wins on the KNL; (2) Across all datasets, the best performer is either MPS on the KNL or BMP on the GPU; and (3) Our optimized algorithms can complete the operation within tens of seconds on billion-edge Twitter graphs, enabling online analytics.

If you use the codes in your research, please kindly cite the following papers.

• Yulin Che, Zhuohang Lai, Shixuan Sun, Qiong Luo, and Yue Wang. 2019. Accelerating All-Edge Common Neighbor Counting on Three Processors. In 48th International Conference on Parallel Processing (ICPP 2019), August 5–8, 2019, Kyoto, Japan. ACM, New York, NY, USA, 10 pages. pdf, code, slides

• Yulin Che, Shixuan Sun, Qiong Luo. 2018. Parallelizing Pruning-based Graph Structural Clustering. In ICPP 2018: 47th International Conference on Parallel Processing, August 13–16, 2018, Eugene, OR, USA. ACM, New York, NY, USA, 10 pages. pdf, code, slides

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• Otherwise, do as follows

git submodule init
git submodule update

• For lemire's CRoaring run amalgamation.sh (to have a single source file)