From f2b1f5f3ae86ae02421e649efdcd519def93d8e8 Mon Sep 17 00:00:00 2001 From: citation-bot Date: Wed, 15 Nov 2023 00:52:00 +0000 Subject: [PATCH] [Citation-Bot] update citation automatically --- .github/citation/citation.json | 2 +- README.md | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/citation/citation.json b/.github/citation/citation.json index 0ebaf11..fdf27fb 100644 --- a/.github/citation/citation.json +++ b/.github/citation/citation.json @@ -1 +1 @@ -{"QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 26, "last update": "2023-11-10"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 25, "last update": "2023-11-10"}, "Learned Low Precision Graph Neural Networks": {"citation": 25, "last update": "2023-11-10"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 11, "last update": "2023-11-10"}, "Improving the Accuracy, 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Large Scale GCN Inference": {"citation": 64, "last update": "2023-11-15"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 21, "last update": "2023-11-15"}, "TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 10, "last update": "2023-11-15"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 23, "last update": "2023-11-15"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 13, "last update": "2023-11-15"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 28, "last update": "2023-11-15"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 1, "last update": "2023-11-15"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 24, "last update": "2023-11-15"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 21, "last update": "2023-11-15"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 11, "last update": "2023-11-15"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 11, "last update": "2023-11-15"}} \ No newline at end of file diff --git a/README.md b/README.md index 61bba14..dbcef9d 100644 --- a/README.md +++ b/README.md @@ -34,15 +34,15 @@ A list of awesome systems for graph neural network (GNN). If you have any commen | :---: | :---: | :---------: | :---: | :----: | |arXiv 2022|Distributed Graph Neural Network Training: A Survey|BUPT| [[paper]](https://arxiv.org/abs/2211.00216)![Scholar citations](https://img.shields.io/badge/Citations-11-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |arXiv 2022|Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis|ETHZ| [[paper]](https://arxiv.org/abs/2205.09702)![Scholar citations](https://img.shields.io/badge/Citations-21-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|CSUR 2022|Computing Graph Neural Networks: A Survey from Algorithms to Accelerators|UPC| [[paper]](https://dl.acm.org/doi/10.1145/3477141)![Scholar citations](https://img.shields.io/badge/Citations-153-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|CSUR 2022|Computing Graph Neural Networks: A Survey from Algorithms to Accelerators|UPC| [[paper]](https://dl.acm.org/doi/10.1145/3477141)![Scholar citations](https://img.shields.io/badge/Citations-154-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| ### GNN Libraries | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | |JMLR 2021|DIG: A Turnkey Library for Diving into Graph Deep Learning Research|TAMU| [[paper]](https://arxiv.org/abs/2103.12608)![Scholar citations](https://img.shields.io/badge/Citations-67-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/divelab/DIG)![GitHub stars](https://img.shields.io/github/stars/divelab/DIG.svg?logo=github&label=Stars)| -|arXiv 2021|CogDL: A Toolkit for Deep Learning on Graphs|THU| [[paper]](https://arxiv.org/abs/2103.00959)![Scholar citations](https://img.shields.io/badge/Citations-14-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/THUDM/cogdl)![GitHub stars](https://img.shields.io/github/stars/THUDM/cogdl.svg?logo=github&label=Stars)| +|arXiv 2021|CogDL: A Toolkit for Deep Learning on Graphs|THU| [[paper]](https://arxiv.org/abs/2103.00959)![Scholar citations](https://img.shields.io/badge/Citations-15-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/THUDM/cogdl)![GitHub stars](https://img.shields.io/github/stars/THUDM/cogdl.svg?logo=github&label=Stars)| |CIM 2021|Graph Neural Networks in TensorFlow and Keras with Spektral|Università della Svizzera italiana| [[paper]](https://arxiv.org/abs/2006.12138)![Scholar citations](https://img.shields.io/badge/Citations-219-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/danielegrattarola/spektral)![GitHub stars](https://img.shields.io/github/stars/danielegrattarola/spektral.svg?logo=github&label=Stars)| |arXiv 2019|Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks|AWS| [[paper]](https://arxiv.org/abs/1909.01315)![Scholar citations](https://img.shields.io/badge/Citations-863-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl)![GitHub stars](https://img.shields.io/github/stars/dmlc/dgl.svg?logo=github&label=Stars)| -|VLDB 2019|AliGraph: A Comprehensive Graph Neural Network Platform|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.14778/3352063.3352127)![Scholar citations](https://img.shields.io/badge/Citations-231-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/alibaba/graph-learn)![GitHub stars](https://img.shields.io/github/stars/alibaba/graph-learn.svg?logo=github&label=Stars)| +|VLDB 2019|AliGraph: A Comprehensive Graph Neural Network Platform|Alibaba| [[paper]](https://dl.acm.org/doi/pdf/10.14778/3352063.3352127)![Scholar citations](https://img.shields.io/badge/Citations-232-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/alibaba/graph-learn)![GitHub stars](https://img.shields.io/github/stars/alibaba/graph-learn.svg?logo=github&label=Stars)| |arXiv 2019|Fast Graph Representation Learning with PyTorch Geometric|TU Dortmund University| [[paper]](https://arxiv.org/abs/1903.02428)![Scholar citations](https://img.shields.io/badge/Citations-3.2k-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/rusty1s/pytorch_geometric)![GitHub stars](https://img.shields.io/github/stars/rusty1s/pytorch_geometric.svg?logo=github&label=Stars)| |arXiv 2018|Relational Inductive Biases, Deep Learning, and Graph Networks|DeepMind| [[paper]](https://arxiv.org/abs/1806.01261)![Scholar citations](https://img.shields.io/badge/Citations-3.1k-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/deepmind/graph_nets)![GitHub stars](https://img.shields.io/github/stars/deepmind/graph_nets.svg?logo=github&label=Stars)| ### GNN Kernels @@ -82,10 +82,10 @@ A list of awesome systems for graph neural network (GNN). If you have any commen |EuroSys 2021|DGCL: An Efficient Communication Library for Distributed GNN Training|CUHK| [[paper]](https://dl.acm.org/doi/abs/10.1145/3447786.3456233)![Scholar citations](https://img.shields.io/badge/Citations-47-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/czkkkkkk/ragdoll)![GitHub stars](https://img.shields.io/github/stars/czkkkkkk/ragdoll.svg?logo=github&label=Stars)| |SC 2020|Reducing Communication in Graph Neural Network Training|UC Berkeley| [[paper]](https://arxiv.org/pdf/2005.03300.pdf)![Scholar citations](https://img.shields.io/badge/Citations-79-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/PASSIONLab/CAGNET)![GitHub stars](https://img.shields.io/github/stars/PASSIONLab/CAGNET.svg?logo=github&label=Stars)| |VLDB 2020|G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs|NUS| [[paper]](http://www.vldb.org/pvldb/vol13/p2813-liu.pdf)![Scholar citations](https://img.shields.io/badge/Citations-37-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/Xtra-Computing/G3)![GitHub stars](https://img.shields.io/github/stars/Xtra-Computing/G3.svg?logo=github&label=Stars)| -|IA3 2020|DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs|AWS| [[paper]](https://arxiv.org/pdf/2010.05337.pdf)![Scholar citations](https://img.shields.io/badge/Citations-86-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl/tree/master/python/dgl/distributed)| -|MLSys 2020|Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc|Stanford| [[paper]](https://proceedings.mlsys.org/paper/2020/file/fe9fc289c3ff0af142b6d3bead98a923-Paper.pdf)![Scholar citations](https://img.shields.io/badge/Citations-167-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/jiazhihao/ROC)![GitHub stars](https://img.shields.io/github/stars/jiazhihao/ROC.svg?logo=github&label=Stars)| +|IA3 2020|DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs|AWS| [[paper]](https://arxiv.org/pdf/2010.05337.pdf)![Scholar citations](https://img.shields.io/badge/Citations-88-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl/tree/master/python/dgl/distributed)| +|MLSys 2020|Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc|Stanford| [[paper]](https://proceedings.mlsys.org/paper/2020/file/fe9fc289c3ff0af142b6d3bead98a923-Paper.pdf)![Scholar citations](https://img.shields.io/badge/Citations-169-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/jiazhihao/ROC)![GitHub stars](https://img.shields.io/github/stars/jiazhihao/ROC.svg?logo=github&label=Stars)| |arXiv 2020|AGL: A Scalable System for Industrial-purpose Graph Machine Learning|Ant Financial Services Group| [[paper]](https://arxiv.org/abs/2003.02454)![Scholar citations](https://img.shields.io/badge/Citations-88-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|ATC 2019|NeuGraph: Parallel Deep Neural Network Computation on Large Graphs|PKU| [[paper]](https://www.usenix.org/system/files/atc19-ma_0.pdf)![Scholar citations](https://img.shields.io/badge/Citations-211-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|ATC 2019|NeuGraph: Parallel Deep Neural Network Computation on Large Graphs|PKU| [[paper]](https://www.usenix.org/system/files/atc19-ma_0.pdf)![Scholar citations](https://img.shields.io/badge/Citations-214-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| ### Training Systems for Scaling Graphs | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | @@ -145,7 +145,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen |MICRO 2021|I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization|PNNL| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3466752.3480113)![Scholar citations](https://img.shields.io/badge/Citations-65-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |arXiv 2021|ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration|SJTU| [[paper]](https://arxiv.org/abs/2107.08709)![Scholar citations](https://img.shields.io/badge/Citations-4-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |TComp 2021|EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks|Chinese Academy of Sciences| [[paper]](https://arxiv.org/abs/1909.00155)![Scholar citations](https://img.shields.io/badge/Citations-139-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|HPCA 2021|GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks|GWU| [[paper]](https://ieeexplore.ieee.org/abstract/document/9407104)![Scholar citations](https://img.shields.io/badge/Citations-91-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|HPCA 2021|GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks|GWU| [[paper]](https://ieeexplore.ieee.org/abstract/document/9407104)![Scholar citations](https://img.shields.io/badge/Citations-92-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |APA 2020|GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks|PKU| [[paper]](http://115.27.240.201/docs/20200915165942122459.pdf)![Scholar citations](https://img.shields.io/badge/Citations-21-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |ASAP 2020|Hardware Acceleration of Large Scale GCN Inference|USC| [[paper]](https://ieeexplore.ieee.org/document/9153263)![Scholar citations](https://img.shields.io/badge/Citations-64-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |DAC 2020|Hardware Acceleration of Graph Neural Networks|UIUC| [[paper]](http://rakeshk.web.engr.illinois.edu/dac20.pdf)![Scholar citations](https://img.shields.io/badge/Citations-103-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)||