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Gunrock/Essentials[1] is a CUDA library for graph-processing designed specifically for the GPU. It uses a high-level, bulk-synchronous/asynchronous, 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.
Before building Gunrock make sure you have CUDA Toolkit[2] 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
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},
}
@InProceedings{Osama:2022:EOP,
author = {Muhammad Osama and Serban D. Porumbescu and John D. Owens},
title = {Essentials of Parallel Graph Analytics},
booktitle = {Proceedings of the Workshop on Graphs,
Architectures, Programming, and Learning},
year = 2022,
series = {GrAPL 2022},
month = may,
pages = {314--317},
doi = {10.1109/IPDPSW55747.2022.00061},
url = {https://escholarship.org/uc/item/2p19z28q},
}
Gunrock is copyright The Regents of the University of California. The library, examples, and all source code are released under Apache 2.0.
- Essentials is intended as a future release of Gunrock. You can read more about in our vision paper: Essentials of Parallel Graph Analytics.
- Preferred CUDA v11.5.1 or higher due to support for stream ordered memory allocators.
Essentials © 2022 The Regents of the University of California
- Programming Model
- Gunrock Operators
- Graph Algorithms
- Getting Essentials
- (GitHub Template)
essentials
project example
- MGPU, Python, Docs (needs review)
- Boolmap Frontier
- Hypergraphs (private)
- Modern CPP Features
- Programming Interface Examples (API)
- Style Guide
- Understanding the code structure
- Git Workflow
-
Debugging with
cuda-memcheck
andcuda-gdb
- Profiling with NVIDIA Nsight Systems and Compute
- Unit testing with GoogleTest
- Performance analysis
- How to write a new graph algorithm
- PageRank: PageRank: From
networkx
togunrock essentials
- How to write parallel operators
- How to add a new graph representation
- How to add a new frontier representation
- How to add multiple GPU support
- How to bind an application to python
- How to use
thrust
/cub
- Writing sparse-matrix dense-vector multiplication using graphs
- Variadic Inheritance
- Polymorphic-Virtual (Diamond) Inheritance
- Need for custom copy constructor
- CUDA-enabled
std::shared_ptr
- Ubuntu
-latest
- Windows
-latest
- Doxygen
- Code Quality