Autonomous, independent management of dynamic graphs on GPUs

In this paper, we present a new, dynamic graph data structure, built to deliver high update rates while keeping a low memory footprint using autonomous memory management directly on the GPU. By transferring the memory management to the GPU, efficient updating of the graph structure and fast initialization times are enabled as no additional memory allocation calls or reallocation procedures are necessary since they are handled directly on the device. In comparison to previous work, this optimized approach allows for significantly lower initialization times (up to 300× faster) and much higher update rates for significant changes to the graph structure and equal rates for small changes. The framework provides different update implementations tailored specifically to different graph properties, enabling over 100 million of updates per second and keeping tens of millions of vertices and hundreds of millions of edges in memory without transferring data back and forth between device and host.

[1]  Shuai Che,et al.  GasCL: A vertex-centric graph model for GPUs , 2014, 2014 IEEE High Performance Extreme Computing Conference (HPEC).

[2]  Ankur Narang,et al.  Fast Community Detection Algorithm with GPUs and Multicore Architectures , 2011, 2011 IEEE International Parallel & Distributed Processing Symposium.

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

[4]  David A. Bader,et al.  STINGER : Spatio-Temporal Interaction Networks and Graphs ( STING ) Extensible Representation , 2009 .

[5]  Michael Garland,et al.  Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

[6]  David A. Bader.  Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA , 2013 .

[7]  Ümit V. Çatalyürek,et al.  Betweenness centrality on GPUs and heterogeneous architectures , 2013, GPGPU@ASPLOS.

[8]  David A. Bader,et al.  Revisiting Edge and Node Parallelism for Dynamic GPU Graph Analytics , 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.

[9]  Bradford M. Beckmann,et al.  BelRed: Constructing GPGPU graph applications with software building blocks , 2014, 2014 IEEE High Performance Extreme Computing Conference (HPEC).

[10]  P. J. Narayanan,et al.  A fast GPU algorithm for graph connectivity , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

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

[12]  David A. Bader,et al.  STINGER: High performance data structure for streaming graphs , 2012, 2012 IEEE Conference on High Performance Extreme Computing.

[13]  David A. Bader,et al.  cuSTINGER: Supporting dynamic graph algorithms for GPUs , 2016, 2016 IEEE High Performance Extreme Computing Conference (HPEC).

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