Hornet

NOTE: The cuhornet repository is a copy of https://github.com/hornet-gt/hornet that is being maintained by the RAPIDS cugraph team while we use it in our library. We currently only use headers to provide the ktruss implementation.

This library does not currently build. Since we only use headers, we are not maintaining the build processes for this library. We expect to drop support for this entirely in early 2024.

This repository provides the Hornet data structure and algorithms on sparse graphs and matrices.

Getting Started

The document is organized as follows:

Requirements
Quick start
Supported graph formats
Code Documentation
Notes
Reporting bugs and contributing
Publications
Hornet Developers
License

Requirements

CUDA toolkit 9 or greater.
GCC or Clang host compiler with support for C++14. Note, the compiler must be compatible with the related CUDA toolkit version. For more information see CUDA Installation Guide.
CMake v3.8 or greater.
64-bit Operating System (Ubuntu 16.04 or above suggested).

Quick start

The following basic steps are required to build and execute Hornet:

git clone --recursive https://github.com/hornet-gt/hornet
export CUDACXX=<path_to_CUDA_nvcc_compiler>
cd hornet/build
cmake ..
make -j

To build HornetsNest (algorithms directory):

cd hornetsnest/build
cmake ..
make -j

By default, the CUDA compiler nvcc uses gcc/g++ found in the current execution search path as host compiler (cc --version to get the default compiler on the actual system). To force a different host compiler for compiling C++ files (*.cpp) you need to set the following environment variables:

CC=<path_to_host_C_compiler>
CXX=<path_to_host_C++_compiler>

Note: host .cpp compiler and host side .cu compiler may be different. The host side compiler must be compatible with the current CUDA Toolkit version installed on the system (see CUDA Installation Guide).

The syntax and the input parameters of Hornet are explained in detail in docs/Syntax.txt. They can also be found by typing ./HornetTest --help.

Supported graph formats

Hornet supports the following graph input formats:

Market (.mtx), The University of Florida Sparse Matrix Collection
Metis (.graph), 10th DIMACS Implementation Challenge
SNAP (.txt), Stanford Network Analysis Project
Dimacs9th (.gr), 9th DIMACS Implementation Challenge
The Koblenz Network Collection (out.< name >), The Koblenz Network Collection
Network Data Repository (.edges), Network Data Repository
Binary (.bin)

Hornet directly deduces the graph structure (directed/undirected) from the input file header.

Hornet allows reading the input graph by using a fixed binary format to speed up the file loading. The binary file is generated by Hornet with the --binary command line option.

Code Documentation

The code documentation is located in the docs directory (doxygen html format).

Notes

Hornet has been checked with the following tools to ensure the code quality:
- clang++ v4.0: warnings
- clang-tidy: warnings and code styles
Hornet has been tested with the following tools: (see CodeCheck)

Reporting bugs and contributing

If you find any bugs please report them by using the repository (github issues panel). We are also ready to engage in improving and extending the framework if you request new features.

Hornet Algorithms

Algorithm	Static	Dynamic
(BFS) Breadth-first Search	yes	on-going
(SSSP) Single-Source Shortest Path	yes	on-going
(CC) Connected Components	yes	on-going
(SCC) Strongly Connected Components	to-do	to-do
(MST) Minimum Spanning Tree	on-going	to-do
(BC) Betweeness Centrality	yes	on-going
(PG) Page Rank	yes	yes
(TC) Triangle Counting	yes	on-going
(KC) Katz Centrality	yes	yes
(MIS) Maximal Independent Set	on-going	to-do
(MF) Maximum Flow	to-do	to-do
(CC) Clustering Coeffient	yes	to-do
(ST) St-Connectivity	to-do	to-do
(TC) Transitive Closure	to-do	to-do
Community Detection	on-going	to-do
Temporal Motif Finding	on-going	to-do
Sparse Vector-Matrix Multiplication	yes	to-do
Jaccard indices	on-going	to-do
Energy/Parity Game	on-going	to-do

Publications

F. Busato, O. Green, N. Bombieri, D. Bader, “Hornet: An Efficient Data Structure for Dynamic Sparse Graphs and Matrices”, IEEE High Performance Extreme Computing Conference (HPEC), Waltham, Massachusetts, 2018 link
Oded Green, David A. Bader, "cuSTINGER: Supporting dynamic graph algorithms for GPUs", IEEE High Performance Extreme Computing Conference (HPEC), 13-15 September, 2016, Waltham, MA, USA, pp. 1-6. link
Fox, O. Green, K. Gabert, X. An, D. Bader, “Fast and Adaptive List Intersections on the GPU”, IEEE High Performance Extreme Computing Conference (HPEC), Waltham, Massachusetts, 2018 *HPEC Graph Challenge Finalist *
O. Green, J. Fox, A. Tripathy, A. Watkins, K. Gabert, E. Kim, X. An, K. Aatish, D. Bader, “Logarithmic Radix Binning and Vectorized Triangle Counting”, IEEE High Performance Extreme Computing Conference (HPEC), Waltham, Massachusetts, 2018 (HPEC Graph Challenge Innovation Award)
A. van der Grinten, E. Bergamini, O. Green, H. Meyerhenke, D. Bader, “Scalable Katz Ranking Computation in Large Dynamic Graphs”, European Symposium on Algorithms, Helsinki, Finland, 2018
Oded Green, James Fox, Euna Kim, Federico Busato, Nicola Bombieri, Kartik Lakhotia, Shijie Zhou, Shreyas Singapura, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna, David A. Bader, "Quickly Finding a Truss in a Haystack", IEEE/Amazon/DARPA Graph Challenge, *Innovation Awards*.
Devavret Makkar, David A. Bader, Oded Green, Exact and Parallel Triangle Counting in Streaming Graphs, IEEE Conference on High Performance Computing, Data, and Analytics (HiPC), 18-21 December 2017, Jaipur, India, pp. 1-10.
A. Tripathy, F. Hohman, D.H Chau, O. Green, "Scalable K-Core Decomposition for Static Graphs Using a Dynamic Graph Data Structure", IEEE International Conference on Big Data, Seattle, Washington, 2018 link

If you find this software useful in academic work, please acknowledge Hornet.

Hornet Developers

Federico Busato, Ph.D. Student, University of Verona (Italy)
Oded Green, Researcher, Georgia Institute of Technology
Federico Busato, Ph.D. Student, University of Verona (Italy)
Oded Green, Researcher, Georgia Institute of Technology
James Fox, Ph.D. Student, Georgia Institute of Technology : Maximal Independent Set, Temporal Motif Finding
Devavret Makkar, Ph.D. Student, Georgia Institute of Technology : Triangle Counting
Elisabetta Bergamini, Ph.D. Student, Karlsruhe Institute of Technology (Germany) : Katz Centrality
Euna Kim, Ph.D. Student, Georgia Institute of Technology : Dynamic PageRank
...

License

BSD 3-Clause License

Copyright (c) 2017, Hornet All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

rapidsai / cuhornet

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