Triangle Counting on GPU Using Fine-Grained Task Distribution

Due to the irregularity of graph data, designing an efficient GPU-based graph algorithm is always a challenging task. Inefficient memory access and work imbalance often limit GPU-based graph computing, even though GPU provides a massively parallelism computing fashion. To address that, in this paper, we propose a fine-grained task distribution strategy for triangle counting task. Extensive experiments and theoretical analysis confirm the superiority of our algorithm over both large real and synthetic graph datasets.

[1]  H. Howie Huang,et al.  iBFS: Concurrent Breadth-First Search on GPUs , 2016, SIGMOD Conference.

[2]  Noga Alon,et al.  Finding and counting given length cycles , 1997, Algorithmica.

[3]  Mauro Bisson,et al.  High Performance Exact Triangle Counting on GPUs , 2017, IEEE Transactions on Parallel and Distributed Systems.

[4]  John R. Gilbert,et al.  Parallel Triangle Counting and Enumeration Using Matrix Algebra , 2015, 2015 IEEE International Parallel and Distributed Processing Symposium Workshop.

[5]  Sudipto Guha,et al.  Improving the Performance of List Intersection , 2009, Proc. VLDB Endow..

[6]  Julian Shun,et al.  Multicore triangle computations without tuning , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[7]  Jeff Heflin,et al.  LUBM: A benchmark for OWL knowledge base systems , 2005, J. Web Semant..

[8]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[9]  Lluís-Miquel Munguía,et al.  Fast triangle counting on the GPU , 2014, IA3 '14.

[10]  David A. Bader,et al.  Fast and Adaptive List Intersections on the GPU , 2018, 2018 IEEE High Performance extreme Computing Conference (HPEC).

[11]  John D. Owens,et al.  A Comparative Study on Exact Triangle Counting Algorithms on the GPU , 2016, HPGP@HPDC.

[12]  Sergei Vassilvitskii,et al.  Counting triangles and the curse of the last reducer , 2011, WWW.

[13]  Bingsheng He,et al.  Fast Subgraph Matching on Large Graphs using Graphics Processors , 2015, DASFAA.

[14]  John D. Owens,et al.  Gunrock: a high-performance graph processing library on the GPU , 2015, PPoPP.

[15]  Fan Chung Graham,et al.  A random graph model for massive graphs , 2000, STOC '00.

[16]  Adam Polak,et al.  Counting Triangles in Large Graphs on GPU , 2015, 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

[17]  Luca Becchetti,et al.  Efficient semi-streaming algorithms for local triangle counting in massive graphs , 2008, KDD.

[18]  David A. Bader,et al.  Logarithmic Radix Binning and Vectorized Triangle Counting , 2018, 2018 IEEE High Performance extreme Computing Conference (HPEC).

[19]  Lei Zou,et al.  Speeding Up Set Intersections in Graph Algorithms using SIMD Instructions , 2018, SIGMOD Conference.

[20]  Dorothea Wagner,et al.  Finding, Counting and Listing All Triangles in Large Graphs, an Experimental Study , 2005, WEA.

[21]  Vladimir Batagelj,et al.  A subquadratic triad census algorithm for large sparse networks with small maximum degree , 2001, Soc. Networks.

[22]  Tamara G. Kolda,et al.  Counting Triangles in Massive Graphs with MapReduce , 2013, SIAM J. Sci. Comput..

[23]  Gang Wang,et al.  Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units , 2011, Proc. VLDB Endow..