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Essentials: High-Performance C++ GPU Graph Analytics

Gunrock/Essentials is a CUDA library for graph-processing designed specifically for the GPU. It uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on vertex or edge frontiers. Gunrock achieves a balance between performance and expressiveness by coupling high-performance GPU computing primitives and optimization strategies, particularly in the area of fine-grained load balancing, with a high-level programming model that allows programmers to quickly develop new graph primitives that scale from one to many GPUs on a node with small code size and minimal GPU programming knowledge.

Quick Start Guide

Before building Gunrock make sure you have CUDA Toolkit 11 or higher[^1] installed on your system. Other external dependencies such as NVIDIA/thrust, NVIDIA/cub, etc. are automatically fetched using cmake.

git clone https://github.com/gunrock/essentials.git
cd essentials
mkdir build && cd build
cmake .. 
make sssp # or for all algorithms, use: make -j$(nproc)
bin/sssp ../datasets/chesapeake/chesapeake.mtx

[^1]: Preferred CUDA v11.2.1 or higher due to support for stream ordered memory allocators (e.g. cudaFreeAsync()).

Getting Started with Gunrock

• Gunrock's Overview
• Gunrock's programming model
• Gunrock's documentation
• Publications and presentations

Essentials vs. Gunrock

Essentials is the future of Gunrock. The idea being to take the lessons learned from Gunrock to a new design, which simplfies the effort it takes to (1) implement graph algorithms, (2) add internal optimizations, (3) conduct future research. One example, in Gunrock SSSP is implemented in 4-5 files with 1000s of lines of code, whereas in essentials it is a single file with ~170 lines of code. Our end goal with essentials is releasing it as a v2.0 for Gunrock.

How to Cite Gunrock

Thank you for citing our work.

@article{Wang:2017:GGG,
  author =	 {Yangzihao Wang and Yuechao Pan and Andrew Davidson
                  and Yuduo Wu and Carl Yang and Leyuan Wang and
                  Muhammad Osama and Chenshan Yuan and Weitang Liu and
                  Andy T. Riffel and John D. Owens},
  title =	 {{G}unrock: {GPU} Graph Analytics},
  journal =	 {ACM Transactions on Parallel Computing},
  year =	 2017,
  volume =	 4,
  number =	 1,
  month =	 aug,
  pages =	 {3:1--3:49},
  doi =		 {10.1145/3108140},
  ee =		 {http://arxiv.org/abs/1701.01170},
  acmauthorize = {http://dl.acm.org/authorize?N45082},
  url =		 {http://escholarship.org/uc/item/9gj6r1dj},
  code =	 {https://github.com/gunrock/gunrock},
  ucdcite =	 {a115},
}

Copyright and License

Gunrock is copyright The Regents of the University of California, 2021. The library, examples, and all source code are released under Apache 2.0.