From 332c403ccb094fe3847e3c3a3c9f0bde343495cb Mon Sep 17 00:00:00 2001 From: citation-bot Date: Tue, 3 Oct 2023 00:48:48 +0000 Subject: [PATCH] [Citation-Bot] update citation automatically --- .github/citation/citation.json | 2 +- README.md | 24 ++++++++++++------------ 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/.github/citation/citation.json b/.github/citation/citation.json index 259aa23..2dde2e8 100644 --- a/.github/citation/citation.json +++ b/.github/citation/citation.json @@ -1 +1 @@ -{"Hardware Acceleration of Large Scale GCN Inference": {"citation": 59, "last update": "2023-09-25"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 19, "last update": "2023-09-25"}, "TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 10, "last update": "2023-09-25"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 21, "last update": "2023-09-25"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 11, "last update": "2023-09-25"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 24, "last update": "2023-09-25"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 1, "last update": "2023-09-25"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 24, "last update": "2023-09-25"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 20, "last update": "2023-09-25"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 9, "last update": "2023-09-25"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 11, "last update": "2023-09-25"}, "HyGCN: A GCN Accelerator with Hybrid Architecture": {"citation": 247, "last update": "2023-09-25"}, "GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching": {"citation": 7, "last update": "2023-09-25"}, "FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems": {"citation": 61, "last update": "2023-09-25"}, "EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression": {"citation": 37, "last update": "2023-09-25"}, "G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency": {"citation": 17, "last update": "2023-09-25"}, "Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine": {"citation": 0, "last update": "2023-09-25"}, "GRIP: A Graph Neural Network Accelerator Architecture": {"citation": 66, "last update": "2023-09-25"}, "GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing": {"citation": 8, "last update": "2023-09-25"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 7, "last update": "2023-09-25"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 24, "last update": "2023-09-25"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 7, "last update": "2023-09-25"}, "Binary Graph Neural Networks": {"citation": 40, "last update": "2023-09-25"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 18, "last update": "2023-09-27"}, "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 117, "last update": "2023-09-27"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 35, "last update": "2023-09-27"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 27, "last update": "2023-09-27"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 82, "last update": "2023-09-27"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 78, "last update": "2023-09-27"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 16, "last update": "2023-09-27"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 73, "last update": "2023-09-27"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 64, "last update": "2023-09-27"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 16, "last update": "2023-09-27"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 5, "last update": "2023-09-27"}, "Understanding and Bridging the Gaps in Current GNN Performance Optimizations": {"citation": 52, "last update": "2023-09-27"}, "MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms": {"citation": 2, "last update": "2023-09-27"}, "GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism": {"citation": 0, "last update": "2023-09-27"}, "GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks": {"citation": 20, "last update": "2023-09-27"}, "Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks": {"citation": 23, "last update": "2023-09-27"}, "Distributed Graph Neural Network Training: A Survey": {"citation": 10, "last update": "2023-09-27"}, "Bi-GCN: Binary Graph Convolutional Network": {"citation": 31, "last update": "2023-09-27"}, "Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs": {"citation": 14, "last update": "2023-09-27"}, "PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network": {"citation": 39, "last update": "2023-09-27"}, "Hardware Acceleration of Graph Neural Networks": {"citation": 101, "last update": "2023-09-27"}, "Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs": {"citation": 13, "last update": "2023-09-27"}, "DGCL: An Efficient Communication Library for Distributed GNN Training": {"citation": 46, "last update": "2023-09-27"}, "TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU": {"citation": 7, "last update": "2023-09-27"}, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings": {"citation": 87, "last update": "2023-09-27"}, "I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization": {"citation": 59, "last update": "2023-09-27"}, "ByteGNN: Efficient Graph Neural Network Training at Large Scale": {"citation": 30, "last update": "2023-09-27"}, "AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 202, "last update": "2023-09-27"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 103, "last update": "2023-10-01"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 12, "last update": "2023-10-01"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing": {"citation": 22, "last update": "2023-10-01"}, "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks": {"citation": 135, "last update": "2023-10-01"}, "Reducing Communication in Graph Neural Network Training": {"citation": 76, "last update": "2023-10-01"}, "fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU": {"citation": 13, "last update": "2023-10-01"}, "Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks": {"citation": 812, "last update": "2023-10-01"}, "Fast Graph Representation Learning with PyTorch Geometric": {"citation": 3070, "last update": "2023-10-01"}, "Relational Inductive Biases, Deep Learning, and Graph Networks": {"citation": 2956, "last update": "2023-10-01"}, "FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training": {"citation": 35, "last update": "2023-10-01"}, "TigerGraph: A Native MPP Graph Database": {"citation": 53, "last update": "2023-10-01"}, "Degree-Quant: Quantization-Aware Training for Graph Neural Networks": {"citation": 110, "last update": "2023-10-01"}, "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling": {"citation": 29, "last update": "2023-10-01"}, "Binarized Graph Neural Network": {"citation": 22, "last update": "2023-10-01"}, "GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration": {"citation": 6, "last update": "2023-10-01"}, "GNNLab: A Factored System for Sample-based GNN Training over GPUs": {"citation": 27, "last update": "2023-10-01"}, "StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing": {"citation": 2, "last update": "2023-10-01"}, "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks": {"citation": 27, "last update": "2023-10-01"}, "$P^3$: Distributed Deep Graph Learning at Scale": {"citation": 82, "last update": "2023-10-01"}, "G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs": {"citation": 36, "last update": "2023-10-01"}, "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication": {"citation": 30, "last update": "2023-10-01"}, "EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization": {"citation": 5, "last update": "2023-10-01"}, "Graph Neural Networks in TensorFlow and Keras with Spektral": {"citation": 211, "last update": "2023-10-02"}, "Efficient Scaling of Dynamic Graph Neural Networks": {"citation": 9, "last update": "2023-10-02"}, "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining": {"citation": 22, "last update": "2023-10-02"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 25, "last update": "2023-10-02"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 24, "last update": "2023-10-02"}, "Learned Low Precision Graph Neural Networks": {"citation": 24, "last update": "2023-10-02"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 11, "last update": "2023-10-02"}, "Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc": {"citation": 165, "last update": "2023-10-02"}, "Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture": {"citation": 42, "last update": "2023-10-02"}, "GraphFM: Improving Large-Scale GNN Training via Feature Momentum": {"citation": 11, "last update": "2023-10-02"}, "Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads": {"citation": 81, "last update": "2023-10-02"}, "PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching": {"citation": 85, "last update": "2023-10-02"}, "NeuGraph: Parallel Deep Neural Network Computation on Large Graphs": {"citation": 208, "last update": "2023-10-02"}, "Computing Graph Neural Networks: A Survey from Algorithms to Accelerators": {"citation": 147, "last update": "2023-10-02"}, "ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration": {"citation": 4, "last update": "2023-10-02"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 89, "last update": "2023-10-02"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 84, "last update": "2023-10-02"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 28, "last update": "2023-10-02"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 14, "last update": "2023-10-02"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 228, "last update": "2023-10-02"}} \ No newline at end of file +{"GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms": {"citation": 117, "last update": "2023-09-27"}, "Seastar: Vertex-Centric Programming for Graph Neural Networks": {"citation": 35, "last update": "2023-09-27"}, "PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm": {"citation": 27, "last update": "2023-09-27"}, "AGL: A Scalable System for Industrial-purpose Graph Machine Learning": {"citation": 82, "last update": "2023-09-27"}, "GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks": {"citation": 78, "last update": "2023-09-27"}, "Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph": {"citation": 16, "last update": "2023-09-27"}, "DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks": {"citation": 73, "last update": "2023-09-27"}, "DIG: A Turnkey Library for Diving into Graph Deep Learning Research": {"citation": 64, "last update": "2023-09-27"}, "Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures": {"citation": 16, "last update": "2023-09-27"}, "Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators": {"citation": 5, "last update": "2023-09-27"}, "Understanding and Bridging the Gaps in Current GNN Performance Optimizations": {"citation": 52, "last update": "2023-09-27"}, "MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms": {"citation": 2, "last update": "2023-09-27"}, "GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism": {"citation": 0, "last update": "2023-09-27"}, "GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks": {"citation": 20, "last update": "2023-09-27"}, "Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks": {"citation": 23, "last update": "2023-09-27"}, "Distributed Graph Neural Network Training: A Survey": {"citation": 10, "last update": "2023-09-27"}, "Bi-GCN: Binary Graph Convolutional Network": {"citation": 31, "last update": "2023-09-27"}, "Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs": {"citation": 14, "last update": "2023-09-27"}, "PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network": {"citation": 39, "last update": "2023-09-27"}, "Hardware Acceleration of Graph Neural Networks": {"citation": 101, "last update": "2023-09-27"}, "Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs": {"citation": 13, "last update": "2023-09-27"}, "DGCL: An Efficient Communication Library for Distributed GNN Training": {"citation": 46, "last update": "2023-09-27"}, "TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU": {"citation": 7, "last update": "2023-09-27"}, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings": {"citation": 87, "last update": "2023-09-27"}, "I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization": {"citation": 59, "last update": "2023-09-27"}, "ByteGNN: Efficient Graph Neural Network Training at Large Scale": {"citation": 30, "last update": "2023-09-27"}, "AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing": {"citation": 202, "last update": "2023-09-27"}, "GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs": {"citation": 103, "last update": "2023-10-01"}, "DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks": {"citation": 12, "last update": "2023-10-01"}, "BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing": {"citation": 22, "last update": "2023-10-01"}, "EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks": {"citation": 135, "last update": "2023-10-01"}, "Reducing Communication in Graph Neural Network Training": {"citation": 76, "last update": "2023-10-01"}, "fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU": {"citation": 13, "last update": "2023-10-01"}, "Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks": {"citation": 812, "last update": "2023-10-01"}, "Fast Graph Representation Learning with PyTorch Geometric": {"citation": 3070, "last update": "2023-10-01"}, "Relational Inductive Biases, Deep Learning, and Graph Networks": {"citation": 2956, "last update": "2023-10-01"}, "FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training": {"citation": 35, "last update": "2023-10-01"}, "TigerGraph: A Native MPP Graph Database": {"citation": 53, "last update": "2023-10-01"}, "Degree-Quant: Quantization-Aware Training for Graph Neural Networks": {"citation": 110, "last update": "2023-10-01"}, "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling": {"citation": 29, "last update": "2023-10-01"}, "Binarized Graph Neural Network": {"citation": 22, "last update": "2023-10-01"}, "GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration": {"citation": 6, "last update": "2023-10-01"}, "GNNLab: A Factored System for Sample-based GNN Training over GPUs": {"citation": 27, "last update": "2023-10-01"}, "StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing": {"citation": 2, "last update": "2023-10-01"}, "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks": {"citation": 27, "last update": "2023-10-01"}, "$P^3$: Distributed Deep Graph Learning at Scale": {"citation": 82, "last update": "2023-10-01"}, "G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs": {"citation": 36, "last update": "2023-10-01"}, "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication": {"citation": 30, "last update": "2023-10-01"}, "EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization": {"citation": 5, "last update": "2023-10-01"}, "Graph Neural Networks in TensorFlow and Keras with Spektral": {"citation": 211, "last update": "2023-10-02"}, "Efficient Scaling of Dynamic Graph Neural Networks": {"citation": 9, "last update": "2023-10-02"}, "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining": {"citation": 22, "last update": "2023-10-02"}, "QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core": {"citation": 25, "last update": "2023-10-02"}, "BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices": {"citation": 24, "last update": "2023-10-02"}, "Learned Low Precision Graph Neural Networks": {"citation": 24, "last update": "2023-10-02"}, "2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters": {"citation": 11, "last update": "2023-10-02"}, "Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc": {"citation": 165, "last update": "2023-10-02"}, "Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture": {"citation": 42, "last update": "2023-10-02"}, "GraphFM: Improving Large-Scale GNN Training via Feature Momentum": {"citation": 11, "last update": "2023-10-02"}, "Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads": {"citation": 81, "last update": "2023-10-02"}, "PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching": {"citation": 85, "last update": "2023-10-02"}, "NeuGraph: Parallel Deep Neural Network Computation on Large Graphs": {"citation": 208, "last update": "2023-10-02"}, "Computing Graph Neural Networks: A Survey from Algorithms to Accelerators": {"citation": 147, "last update": "2023-10-02"}, "ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration": {"citation": 4, "last update": "2023-10-02"}, "GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks": {"citation": 89, "last update": "2023-10-02"}, "DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs": {"citation": 84, "last update": "2023-10-02"}, "SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization": {"citation": 28, "last update": "2023-10-02"}, "CogDL: A Toolkit for Deep Learning on Graphs": {"citation": 14, "last update": "2023-10-02"}, "AliGraph: A Comprehensive Graph Neural Network Platform": {"citation": 228, "last update": "2023-10-02"}, "Hardware Acceleration of Large Scale GCN Inference": {"citation": 61, "last update": "2023-10-03"}, "Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks": {"citation": 20, "last update": "2023-10-03"}, "TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning": {"citation": 10, "last update": "2023-10-03"}, "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design": {"citation": 22, "last update": "2023-10-03"}, "Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network": {"citation": 12, "last update": "2023-10-03"}, "Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective": {"citation": 24, "last update": "2023-10-03"}, "DRGN: a dynamically reconfigurable accelerator for graph neural networks": {"citation": 1, "last update": "2023-10-03"}, "Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs": {"citation": 24, "last update": "2023-10-03"}, "Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs": {"citation": 20, "last update": "2023-10-03"}, "Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques": {"citation": 10, "last update": "2023-10-03"}, "Rubik: A Hierarchical Architecture for Efficient Graph Learning": {"citation": 11, "last update": "2023-10-03"}, "HyGCN: A GCN Accelerator with Hybrid Architecture": {"citation": 248, "last update": "2023-10-03"}, "GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching": {"citation": 8, "last update": "2023-10-03"}, "FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems": {"citation": 62, "last update": "2023-10-03"}, "EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression": {"citation": 38, "last update": "2023-10-03"}, "G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency": {"citation": 18, "last update": "2023-10-03"}, "Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine": {"citation": 0, "last update": "2023-10-03"}, "GRIP: A Graph Neural Network Accelerator Architecture": {"citation": 67, "last update": "2023-10-03"}, "GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing": {"citation": 8, "last update": "2023-10-03"}, "FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming": {"citation": 7, "last update": "2023-10-03"}, "ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks": {"citation": 25, "last update": "2023-10-03"}, "GIST: Distributed Training for Large-Scale Graph Convolutional Networks": {"citation": 7, "last update": "2023-10-03"}, "Binary Graph Neural Networks": {"citation": 40, "last update": "2023-10-03"}, "Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis": {"citation": 18, "last update": "2023-10-03"}} \ No newline at end of file diff --git a/README.md b/README.md index 63d0e99..fe7e588 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen | :---: | :---: | :---------: | :---: | :----: | |MLSys 2022|Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph|ShanghaiTech| [[paper]](https://proceedings.mlsys.org/paper/2022/file/a87ff679a2f3e71d9181a67b7542122c-Paper.pdf)![Scholar citations](https://img.shields.io/badge/Citations-16-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/xiezhq-hermann/graphiler)![GitHub stars](https://img.shields.io/github/stars/xiezhq-hermann/graphiler.svg?logo=github&label=Stars)| |EuroSys 2021|Seastar: Vertex-Centric Programming for Graph Neural Networks|CUHK| [[paper]](http://www.cse.cuhk.edu.hk/~jcheng/papers/seastar_eurosys21.pdf)![Scholar citations](https://img.shields.io/badge/Citations-35-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|SC 2020|FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems|Cornell| [[paper]](https://arxiv.org/pdf/2008.11359.pdf)![Scholar citations](https://img.shields.io/badge/Citations-61-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dglai/FeatGraph)![GitHub stars](https://img.shields.io/github/stars/dglai/FeatGraph.svg?logo=github&label=Stars)| +|SC 2020|FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems|Cornell| [[paper]](https://arxiv.org/pdf/2008.11359.pdf)![Scholar citations](https://img.shields.io/badge/Citations-62-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dglai/FeatGraph)![GitHub stars](https://img.shields.io/github/stars/dglai/FeatGraph.svg?logo=github&label=Stars)| ### Distributed GNN Training Systems | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | @@ -70,7 +70,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen |MLSys 2022|Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs|Intel| [[paper]](https://arxiv.org/abs/2111.06483)![Scholar citations](https://img.shields.io/badge/Citations-14-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/IntelLabs/SAR)![GitHub stars](https://img.shields.io/github/stars/IntelLabs/SAR.svg?logo=github&label=Stars)| |WWW 2022|PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm|PKU| [[paper]](https://dl.acm.org/doi/abs/10.1145/3485447.3511986)![Scholar citations](https://img.shields.io/badge/Citations-27-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |ICLR 2022|PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication|Rice| [[paper]](https://openreview.net/pdf?id=kSwqMH0zn1F)![Scholar citations](https://img.shields.io/badge/Citations-30-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/RICE-EIC/PipeGCN)![GitHub stars](https://img.shields.io/github/stars/RICE-EIC/PipeGCN.svg?logo=github&label=Stars)| -|ICLR 2022|Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks|PSU| [[paper]](https://openreview.net/pdf?id=FndDxSz3LxQ)![Scholar citations](https://img.shields.io/badge/Citations-19-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/MortezaRamezani/llcg)![GitHub stars](https://img.shields.io/github/stars/MortezaRamezani/llcg.svg?logo=github&label=Stars)| +|ICLR 2022|Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks|PSU| [[paper]](https://openreview.net/pdf?id=FndDxSz3LxQ)![Scholar citations](https://img.shields.io/badge/Citations-20-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/MortezaRamezani/llcg)![GitHub stars](https://img.shields.io/github/stars/MortezaRamezani/llcg.svg?logo=github&label=Stars)| |arXiv 2021|Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs|AWS| [[paper]](https://arxiv.org/pdf/2112.15345.pdf)![Scholar citations](https://img.shields.io/badge/Citations-13-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |SC 2021|DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks|Intel| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3458817.3480856)![Scholar citations](https://img.shields.io/badge/Citations-73-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dmlc/dgl/pull/3024)| |SC 2021|Efficient Scaling of Dynamic Graph Neural Networks|IBM| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3458817.3480858)![Scholar citations](https://img.shields.io/badge/Citations-9-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| @@ -98,7 +98,7 @@ A list of awesome systems for graph neural network (GNN). If you have any commen | 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-5-_.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-37-_.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)| +|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-38-_.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-25-_.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-40-_.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-31-_.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)| @@ -121,37 +121,37 @@ A list of awesome systems for graph neural network (GNN). If you have any commen ### GNN Training Accelerators | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | -|ISCA 2022|Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques|UIUC| [[paper]](http://iacoma.cs.uiuc.edu/iacoma-papers/isca22.pdf)![Scholar citations](https://img.shields.io/badge/Citations-9-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|ISCA 2022|Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network|Alibaba| [[paper]](https://dl.acm.org/doi/abs/10.1145/3470496.3527439)![Scholar citations](https://img.shields.io/badge/Citations-11-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|ISCA 2022|Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques|UIUC| [[paper]](http://iacoma.cs.uiuc.edu/iacoma-papers/isca22.pdf)![Scholar citations](https://img.shields.io/badge/Citations-10-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|ISCA 2022|Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network|Alibaba| [[paper]](https://dl.acm.org/doi/abs/10.1145/3470496.3527439)![Scholar citations](https://img.shields.io/badge/Citations-12-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |arXiv 2021|GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing|PKU| [[paper]](https://arxiv.org/abs/2111.00680)![Scholar citations](https://img.shields.io/badge/Citations-8-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|DATE 2021|ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks|WSU| [[paper]](https://arxiv.org/abs/2102.07959)![Scholar citations](https://img.shields.io/badge/Citations-24-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|DATE 2021|ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks|WSU| [[paper]](https://arxiv.org/abs/2102.07959)![Scholar citations](https://img.shields.io/badge/Citations-25-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |TCAD 2021|Rubik: A Hierarchical Architecture for Efficient Graph Learning|Chinese Academy of Sciences| [[paper]](https://arxiv.org/pdf/2009.12495.pdf)![Scholar citations](https://img.shields.io/badge/Citations-11-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |FPGA 2020|GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms|USC| [[paper]](https://dl.acm.org/doi/pdf/10.1145/3373087.3375312)![Scholar citations](https://img.shields.io/badge/Citations-117-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/GraphSAINT/GraphACT)![GitHub stars](https://img.shields.io/github/stars/GraphSAINT/GraphACT.svg?logo=github&label=Stars)| ### GNN Inference Accelerators | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | |JAIHC 2022|DRGN: a dynamically reconfigurable accelerator for graph neural networks|XJTU| [[paper]](https://link.springer.com/article/10.1007/s12652-022-04402-x)![Scholar citations](https://img.shields.io/badge/Citations-1-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|DAC 2022|GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching|UMN| [[paper]](https://arxiv.org/abs/2105.10554)![Scholar citations](https://img.shields.io/badge/Citations-7-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|DAC 2022|GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching|UMN| [[paper]](https://arxiv.org/abs/2105.10554)![Scholar citations](https://img.shields.io/badge/Citations-8-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |IPDPS 2022|Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators|GaTech| [[paper]](https://arxiv.org/abs/2103.07977)![Scholar citations](https://img.shields.io/badge/Citations-5-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/stonne-simulator/omega)![GitHub stars](https://img.shields.io/github/stars/stonne-simulator/omega.svg?logo=github&label=Stars)| |arXiv 2022|FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming|GaTech| [[paper]](https://arxiv.org/abs/2204.13103)![Scholar citations](https://img.shields.io/badge/Citations-7-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |CICC 2022|StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing|UCLA| [[paper]](https://web.cs.ucla.edu/~atefehsz/publication/StreamGCN-CICC22.pdf)![Scholar citations](https://img.shields.io/badge/Citations-2-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |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-16-_.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-21-_.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)| +|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-22-_.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-6-_.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-12-_.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-24-_.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-10-_.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-17-_.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-18-_.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-59-_.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-135-_.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-89-_.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-20-_.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-59-_.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-61-_.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-101-_.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-202-_.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-66-_.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-247-_.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-67-_.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-248-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| ## Contribute We welcome contributions to [this repository](https://github.com/chwan1016/awesome-gnn-systems). To add new papers to this list, please update JSON files under `./res/papers/`. Our bots will update the paper list in `README.md` automatically. The citations of newly added papers will be updated within one day.