Gunrock is a CUDA library for graph primitives that refactors, integrates, and generalizes best-of-class GPU implementations of breadth-first search, connected components, and betweenness centrality into a unified code base useful for future development of high-performance GPU graph primitives.
Homepage for Gunrock: http://gunrock.github.io/
For Frequently Asked Questions, see FAQ.
The "tests" subdirectory included with Gunrock has a comprehensive test application for all the functionality of Gunrock.
For the programming model we use in Gunrock, see Programming Model.
We have also provided a code walkthrough of a simple example.
To report Gunrock bugs or request features, please file an issue directly using Github.
This release (0.1) has only been tested on Linux Mint 15 (64-bit) with CUDA 5.5 installed. We expect Gunrock to build and run correctly on other 64-bit and 32-bit Linux distributions. The current release (0.1) does not support any other platforms.
Gunrock has not been tested with any CUDA version < 5.5.
The CPU validity code for connected component and betweenness centrality uses Boost Graph Library v1.53.0.
Gunrock is implemented in CUDA C/C++. It requires the CUDA Toolkit. Please see the NVIDIA CUDA homepage to download CUDA as well as the CUDA Programming Guide and CUDA SDK, which includes many CUDA code examples. Please refer to NVIDIA CUDA Getting Started Guide for Linux for detailed information.
Gunrock aims to provide a core set of vertex-centric or edge-centric operators for solving graph related problems and use these parallel-friendly abstractions to improve programmer productivity while maintaining high performance.
Framework: The structure of the operator code in Gunrock may change significantly during near-term future development. Generally we want to find the right set of operators that can abstract most graph primitives while delivering high performance.
Primitives: Our near-term goal is to implement minimal spanning tree algorithm, build better support for bipartite graph algorithms, and explore community detection algorithms. The long term goal includes algorithms on dynamic graphs, priority queue support, graph partitioning and multi-GPU algorithms.
Yangzihao Wang, University of California, Davis
Yuechao Pan, University of California, Davis
Yuduo Wu, University of California, Davis
Andy Riffel, University of California, Davis
John Owens, University of California, Davis
Thanks to the following developers who contributed code: The connected-component implementation was derived from code written by Jyothish Soman, Kothapalli Kishore, and P. J. Narayanan and described in their IPDPSW '10 paper A Fast GPU Algorithm for Graph Connectivity (DOI). The breadth-first search implementation and many of the utility functions in Gunrock are derived from the b40c library of Duane Merrill. The algorithm is described in his PPoPP '12 paper Scalable GPU Graph Traversal (DOI). Thanks to Erich Elsen and Vishal Vaidyanathan from Royal Caliber for their discussion on library development and the dataset auto-generating code.
This work was funded by the DARPA XDATA program under AFRL Contract FA8750-13-C-0002 and by NSF awards CCF-1017399 and OCI-1032859. Our XDATA principal investigator is Eric Whyne of Data Tactics Corporation and our DARPA program manager is Dr. Christopher White.
Gunrock is copyright The Regents of the University of California, 2013. The library, examples, and all source code are released under Apache 2.0.