From 88106719e5698056fb652ef8ed058589e9639fac Mon Sep 17 00:00:00 2001 From: citation-bot Date: Sat, 23 Nov 2024 01:06:00 +0000 Subject: [PATCH] [Citation-Bot] update citation automatically --- .github/citation/citation.json | 2 +- README.md | 20 ++++++++++---------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/.github/citation/citation.json b/.github/citation/citation.json index cfddac8..46f1f2a 100644 --- a/.github/citation/citation.json +++ b/.github/citation/citation.json @@ -1 +1 @@ -{"AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 289, "last update": "2024-11-19"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 160, "last update": "2024-11-19"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 31, "last update": "2024-11-19"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and 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Computation-aware Caching": {"citation": 170, "last update": "2024-11-23"}, "NeuGraph: Parallel Deep Neural Network Computation on Large Graphs": {"citation": 282, "last update": "2024-11-23"}, "Computing Graph Neural Networks: A Survey from Algorithms to Accelerators": {"citation": 258, "last update": "2024-11-23"}, "ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration": {"citation": 5, "last update": "2024-11-23"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 140, "last update": "2024-11-23"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 136, "last update": "2024-11-23"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 52, "last update": "2024-11-23"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 2, "last update": "2024-11-23"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 316, "last update": "2024-11-23"}, "BeaconGNN: Large-Scale GNN Acceleration with Out-of-Order Streaming In-Storage Computing": {"citation": 3, "last update": "2024-11-23"}, "In situ neighborhood sampling for large-scale GNN training": {"citation": 0, "last update": "2024-11-23"}} \ No newline at end of file diff --git a/README.md b/README.md index 8f7cfae..fd3d92b 100644 --- a/README.md +++ b/README.md @@ -88,7 +88,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen |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-136-_.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-251-_.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-128-_.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-281-_.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-282-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| ### Training Systems for Scaling Graphs | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | @@ -100,19 +100,19 @@ A list of awesome systems for graph neural network (GNN). If you have any commen |ISCA 2022|SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures|KAIST| [[paper]](https://dl.acm.org/doi/10.1145/3470496.3527391)![Scholar citations](https://img.shields.io/badge/Citations-43-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |ICML 2022|GraphFM: Improving Large-Scale GNN Training via Feature Momentum|TAMU| [[paper]](https://arxiv.org/abs/2206.07161)![Scholar citations](https://img.shields.io/badge/Citations-29-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/divelab/DIG/tree/dig-stable/dig/lsgraph)| |ICML 2021|GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings|TU Dortmund University| [[paper]](https://arxiv.org/abs/2106.05609)![Scholar citations](https://img.shields.io/badge/Citations-165-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/rusty1s/pyg_autoscale)![GitHub stars](https://img.shields.io/github/stars/rusty1s/pyg_autoscale.svg?logo=github&label=Stars)| -|OSDI 2021|GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs|UCSB| [[paper]](https://www.usenix.org/system/files/osdi21-wang-yuke.pdf)![Scholar citations](https://img.shields.io/badge/Citations-160-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/OSDI21_AE)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/OSDI21_AE.svg?logo=github&label=Stars)| +|OSDI 2021|GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs|UCSB| [[paper]](https://www.usenix.org/system/files/osdi21-wang-yuke.pdf)![Scholar citations](https://img.shields.io/badge/Citations-161-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/OSDI21_AE)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/OSDI21_AE.svg?logo=github&label=Stars)| ### Quantized GNNs | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | |Neurocomputing 2022|EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization|ZJU| [[paper]](https://www.sciencedirect.com/science/article/abs/pii/S0925231222008293)![Scholar citations](https://img.shields.io/badge/Citations-11-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/Lyun-Huang/EPQuant)![GitHub stars](https://img.shields.io/github/stars/Lyun-Huang/EPQuant.svg?logo=github&label=Stars)| |ICLR 2022|EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression|Rice| [[paper]](https://openreview.net/pdf?id=vkaMaq95_rX)![Scholar citations](https://img.shields.io/badge/Citations-60-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/warai-0toko/Exact)![GitHub stars](https://img.shields.io/github/stars/warai-0toko/Exact.svg?logo=github&label=Stars)| -|PPoPP 2022|QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core|UCSB| [[paper]](https://arxiv.org/abs/2111.09547)![Scholar citations](https://img.shields.io/badge/Citations-44-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/PPoPP22_QGTC)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/PPoPP22_QGTC.svg?logo=github&label=Stars)| +|PPoPP 2022|QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core|UCSB| [[paper]](https://arxiv.org/abs/2111.09547)![Scholar citations](https://img.shields.io/badge/Citations-45-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/PPoPP22_QGTC)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/PPoPP22_QGTC.svg?logo=github&label=Stars)| |CVPR 2021|Binary Graph Neural Networks|ICL| [[paper]](https://arxiv.org/abs/2012.15823)![Scholar citations](https://img.shields.io/badge/Citations-57-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/mbahri/binary_gnn)![GitHub stars](https://img.shields.io/github/stars/mbahri/binary_gnn.svg?logo=github&label=Stars)| |CVPR 2021|Bi-GCN: Binary Graph Convolutional Network|Beihang University| [[paper]](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.html)![Scholar citations](https://img.shields.io/badge/Citations-57-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/bywmm/Bi-GCN)![GitHub stars](https://img.shields.io/github/stars/bywmm/Bi-GCN.svg?logo=github&label=Stars)| |EuroMLSys 2021|Learned Low Precision Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2009.09232)![Scholar citations](https://img.shields.io/badge/Citations-39-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |World Wide Web 2021|Binarized Graph Neural Network|UTS| [[paper]](https://arxiv.org/pdf/2004.11147.pdf)![Scholar citations](https://img.shields.io/badge/Citations-33-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |ICLR 2021|Degree-Quant: Quantization-Aware Training for Graph Neural Networks|Cambridge| [[paper]](https://arxiv.org/abs/2008.05000)![Scholar citations](https://img.shields.io/badge/Citations-192-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/camlsys/degree-quant)![GitHub stars](https://img.shields.io/github/stars/camlsys/degree-quant.svg?logo=github&label=Stars)| -|ICTAI 2020|SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization|UCSB| [[paper]](https://ieeexplore.ieee.org/abstract/document/9288186/)![Scholar citations](https://img.shields.io/badge/Citations-49-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/SGQuant)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/SGQuant.svg?logo=github&label=Stars)| +|ICTAI 2020|SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization|UCSB| [[paper]](https://ieeexplore.ieee.org/abstract/document/9288186/)![Scholar citations](https://img.shields.io/badge/Citations-52-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YukeWang96/SGQuant)![GitHub stars](https://img.shields.io/github/stars/YukeWang96/SGQuant.svg?logo=github&label=Stars)| ### GNN Dataloaders | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | @@ -123,7 +123,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen |PPoPP 2021|Understanding and Bridging the Gaps in Current GNN Performance Optimizations|THU| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3437801.3441585)![Scholar citations](https://img.shields.io/badge/Citations-83-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/xxcclong/GNN-Computing)![GitHub stars](https://img.shields.io/github/stars/xxcclong/GNN-Computing.svg?logo=github&label=Stars)| |VLDB 2021|Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture|UIUC| [[paper]](https://arxiv.org/abs/2103.03330)![Scholar citations](https://img.shields.io/badge/Citations-67-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/K-Wu/pytorch-direct_dgl)![GitHub stars](https://img.shields.io/github/stars/K-Wu/pytorch-direct_dgl.svg?logo=github&label=Stars)| |TPDS 2021|Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs|USTC| [[paper]](https://gnnsys.github.io/papers/GNNSys21_paper_8.pdf)![Scholar citations](https://img.shields.io/badge/Citations-36-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)| -|SoCC 2020|PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching|USTC| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3419111.3421281)![Scholar citations](https://img.shields.io/badge/Citations-169-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)| +|SoCC 2020|PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching|USTC| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3419111.3421281)![Scholar citations](https://img.shields.io/badge/Citations-170-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/zhiqi-0/PaGraph)![GitHub stars](https://img.shields.io/github/stars/zhiqi-0/PaGraph.svg?logo=github&label=Stars)| |arXiv 2019|TigerGraph: A Native MPP Graph Database|UCSD| [[paper]](https://arxiv.org/pdf/1901.08248.pdf)![Scholar citations](https://img.shields.io/badge/Citations-81-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| ### GNN Training Accelerators | Venue | Title | Affiliation |       Link       |   Source   | @@ -145,18 +145,18 @@ A list of awesome systems for graph neural network (GNN). If you have any commen |HPCA 2022|Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures|HUST| [[paper]](https://ieeexplore.ieee.org/document/9773267)![Scholar citations](https://img.shields.io/badge/Citations-44-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |HPCA 2022|GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design|Rice, PNNL| [[paper]](https://arxiv.org/abs/2112.11594)![Scholar citations](https://img.shields.io/badge/Citations-53-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/GCoD)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/GCoD.svg?logo=github&label=Stars)| |arXiv 2022|GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration|GaTech| [[paper]](https://arxiv.org/abs/2201.08475)![Scholar citations](https://img.shields.io/badge/Citations-17-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|DAC 2021|DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks|Hunan University| [[paper]](https://ieeexplore.ieee.org/document/9586298)![Scholar citations](https://img.shields.io/badge/Citations-31-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|DAC 2021|BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices|PKU| [[paper]](https://arxiv.org/abs/2104.06214)![Scholar citations](https://img.shields.io/badge/Citations-36-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|DAC 2021|DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks|Hunan University| [[paper]](https://ieeexplore.ieee.org/document/9586298)![Scholar citations](https://img.shields.io/badge/Citations-32-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|DAC 2021|BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices|PKU| [[paper]](https://arxiv.org/abs/2104.06214)![Scholar citations](https://img.shields.io/badge/Citations-37-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |DAC 2021|TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning|Chinese Academy of Sciences| [[paper]](https://ieeexplore.ieee.org/abstract/document/9586193)![Scholar citations](https://img.shields.io/badge/Citations-14-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |ICCAD 2021|G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency|Rice| [[paper]](https://arxiv.org/abs/2109.08983)![Scholar citations](https://img.shields.io/badge/Citations-32-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |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-114-_.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-5-_.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-191-_.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-136-_.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-193-_.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-140-_.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-23-_.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-84-_.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-142-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|MICRO 2020|AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing|PNNL| [[paper]](https://ieeexplore.ieee.org/abstract/document/9252000)![Scholar citations](https://img.shields.io/badge/Citations-289-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|MICRO 2020|AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing|PNNL| [[paper]](https://ieeexplore.ieee.org/abstract/document/9252000)![Scholar citations](https://img.shields.io/badge/Citations-293-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |arXiv 2020|GRIP: A Graph Neural Network Accelerator Architecture|Stanford| [[paper]](https://arxiv.org/pdf/2007.13828.pdf)![Scholar citations](https://img.shields.io/badge/Citations-99-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |HPCA 2020|HyGCN: A GCN Accelerator with Hybrid Architecture|UCSB| [[paper]](https://arxiv.org/pdf/2001.02514.pdf)![Scholar citations](https://img.shields.io/badge/Citations-347-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| ## Contribute