Gunrock documentation

Introduction

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.

Home Page

Homepage for Gunrock: http://gunrock.github.io/

Getting Started with Gunrock

For Frequently Asked Questions, see FAQ.

For information on building Gunrock, see Building Gunrock and refer to Operating System Support and Requirements.

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.

Reporting Problems

To report Gunrock bugs or request features, please file an issue directly using Github.

Operating System Support and Requirements

This release (0.2) has only been tested on Linux Mint 15 (64-bit) with CUDA 6.0 installed. We expect Gunrock to build and run correctly on other 64-bit and 32-bit Linux distributions, Mac OS, and Windows.

Requirements

Gunrock has not been tested with any CUDA version < 5.5.

Several graph primitives' CPU validation code uses Boost Graph Library v1.53.0.

CUDA

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.

Design Goals

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.

Road Map

• 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.

Credits

Gunrock Developers

• 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

Acknowledgements

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 Copyright and Software License

Gunrock is copyright The Regents of the University of California, 2013. The library, examples, and all source code are released under Apache 2.0.