Awesome Graph Neural Network Systems
A list of awesome systems for graph neural network (GNN). If you have any comment, please create an issue or pull request .
Venue
Title
Affiliation
Link
Source
arXiv 2022
Distributed Graph Neural Network Training: A Survey
BUPT
[paper]
arXiv 2022
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis
ETHZ
[paper]
CSUR 2022
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
UPC
[paper]
Venue
Title
Affiliation
Link
Source
JMLR 2021
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
TAMU
[paper]
[code]
arXiv 2021
CogDL: A Toolkit for Deep Learning on Graphs
THU
[paper]
[code]
CIM 2021
Graph Neural Networks in TensorFlow and Keras with Spektral
UniversitĂ della Svizzera italiana
[paper]
[code]
arXiv 2019
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
AWS
[paper]
[code]
VLDB 2019
AliGraph: A Comprehensive Graph Neural Network Platform
Alibaba
[paper]
[code]
arXiv 2019
Fast Graph Representation Learning with PyTorch Geometric
TU Dortmund University
[paper]
[code]
arXiv 2018
Relational Inductive Biases, Deep Learning, and Graph Networks
DeepMind
[paper]
[code]
Venue
Title
Affiliation
Link
Source
MLSys 2022
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
THU
[paper]
[code]
HPDC 2022
TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU
GW
[paper]
IPDPS 2021
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks
Indiana University Bloomington
[paper]
[code]
SC 2020
GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks
THU
[paper]
[code]
ICCAD 2020
fuseGNN: Accelerating Graph Convolutional Neural Network Training on GPGPU
UCSB
[paper]
[code]
IPDPS 2020
PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network
PKU
[paper]
Venue
Title
Affiliation
Link
Source
MLSys 2022
Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph
ShanghaiTech
[paper]
[code]
EuroSys 2021
Seastar: Vertex-Centric Programming for Graph Neural Networks
CUHK
[paper]
SC 2020
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Cornell
[paper]
[code]
Distributed GNN Training Systems
Venue
Title
Affiliation
Link
Source
VLDB 2022
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks
HKUST
[paper]
[code]
MLSys 2022
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Rice, UIUC
[paper]
[code]
MLSys 2022
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs
Intel
[paper]
[code]
WWW 2022
PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm
PKU
[paper]
ICLR 2022
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Rice
[paper]
[code]
ICLR 2022
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
PSU
[paper]
[code]
arXiv 2021
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
AWS
[paper]
SC 2021
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Intel
[paper]
[code]
SC 2021
Efficient Scaling of Dynamic Graph Neural Networks
IBM
[paper]
CLUSTER 2021
2PGraph: Accelerating GNN Training over Large Graphs on GPU Clusters
NUDT
[paper]
OSDI 2021
$P^3$ : Distributed Deep Graph Learning at Scale
MSR
[paper]
OSDI 2021
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
UCLA
[paper]
[code]
arXiv 2021
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
Rice
[paper]
EuroSys 2021
FlexGraph: A Flexible and Efficient Distributed Framework for GNN Training
Alibaba
[paper]
EuroSys 2021
DGCL: An Efficient Communication Library for Distributed GNN Training
CUHK
[paper]
[code]
SC 2020
Reducing Communication in Graph Neural Network Training
UC Berkeley
[paper]
[code]
VLDB 2020
G$^3$: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs
NUS
[paper]
[code]
IA3 2020
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
AWS
[paper]
[code]
MLSys 2020
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc
Stanford
[paper]
[code]
arXiv 2020
AGL: A Scalable System for Industrial-purpose Graph Machine Learning
Ant Financial Services Group
[paper]
ATC 2019
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs
PKU
[paper]
Training Systems for Scaling Graphs
Venue
Title
Affiliation
Link
Source
EuroSys 2023
Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine
UW–Madison
[paper]
[code]
VLDB 2022
ByteGNN: Efficient Graph Neural Network Training at Large Scale
ByteDance
[paper]
ICML 2022
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
TAMU
[paper]
[code]
ICML 2021
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
TU Dortmund University
[paper]
[code]
OSDI 2021
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
UCSB
[paper]
[code]
Venue
Title
Affiliation
Link
Source
Neurocomputing 2022
EPQuant: A Graph Neural Network Compression Approach Based on Product Quantization
ZJU
[paper]
[code]
ICLR 2022
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Rice
[paper]
[code]
PPoPP 2022
QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core
UCSB
[paper]
[code]
CVPR 2021
Binary Graph Neural Networks
ICL
[paper]
[code]
CVPR 2021
Bi-GCN: Binary Graph Convolutional Network
Beihang University
[paper]
[code]
EuroMLSys 2021
Learned Low Precision Graph Neural Networks
Cambridge
[paper]
World Wide Web 2021
Binarized Graph Neural Network
UTS
[paper]
ICLR 2021
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Cambridge
[paper]
[code]
ICTAI 2020
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
UCSB
[paper]
[code]
Venue
Title
Affiliation
Link
Source
NSDI 2023
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
ByteDance
[paper]
MLSys 2022
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
MIT
[paper]
[code]
EuroSys 2022
GNNLab: A Factored System for Sample-based GNN Training over GPUs
SJTU
[paper]
[code]
KDD 2021
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
UCLA
[paper]
PPoPP 2021
Understanding and Bridging the Gaps in Current GNN Performance Optimizations
THU
[paper]
[code]
VLDB 2021
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
UIUC
[paper]
[code]
TPDS 2021
Efficient Data Loader for Fast Sampling-Based GNN Training on Large Graphs
USTC
[paper]
[code]
SoCC 2020
PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching
USTC
[paper]
[code]
arXiv 2019
TigerGraph: A Native MPP Graph Database
UCSD
[paper]
GNN Training Accelerators
Venue
Title
Affiliation
Link
Source
ISCA 2022
Graphite: Optimizing Graph Neural Networks on CPUs Through Cooperative Software-Hardware Techniques
UIUC
[paper]
ISCA 2022
Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network
Alibaba
[paper]
arXiv 2021
GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing
PKU
[paper]
DATE 2021
ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks
WSU
[paper]
TCAD 2021
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Chinese Academy of Sciences
[paper]
FPGA 2020
GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms
USC
[paper]
[code]
GNN Inference Accelerators
Venue
Title
Affiliation
Link
Source
JAIHC 2022
DRGN: a dynamically reconfigurable accelerator for graph neural networks
XJTU
[paper]
DAC 2022
GNNIE: GNN Inference Engine with Load-balancing and Graph-specific Caching
UMN
[paper]
IPDPS 2022
Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators
GaTech
[paper]
[code]
arXiv 2022
FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming
GaTech
[paper]
CICC 2022
StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing
UCLA
[paper]
HPCA 2022
Accelerating Graph Convolutional Networks Using Crossbar-based Processing-In-Memory Architectures
HUST
[paper]
HPCA 2022
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design
Rice, PNNL
[paper]
[code]
arXiv 2022
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
GaTech
[paper]
DAC 2021
DyGNN: Algorithm and Architecture Support of vertex Dynamic Pruning for Graph Neural Networks
Hunan University
[paper]
DAC 2021
BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices
PKU
[paper]
DAC 2021
TARe: Task-Adaptive in-situ ReRAM Computing for Graph Learning
Chinese Academy of Sciences
[paper]
ICCAD 2021
G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency
Rice
[paper]
MICRO 2021
I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization
PNNL
[paper]
arXiv 2021
ZIPPER: Exploiting Tile- and Operator-level Parallelism for General and Scalable Graph Neural Network Acceleration
SJTU
[paper]
TComp 2021
EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
Chinese Academy of Sciences
[paper]
HPCA 2021
GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks
GWU
[paper]
APA 2020
GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks
PKU
[paper]
ASAP 2020
Hardware Acceleration of Large Scale GCN Inference
USC
[paper]
DAC 2020
Hardware Acceleration of Graph Neural Networks
UIUC
[paper]
MICRO 2020
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
PNNL
[paper]
arXiv 2020
GRIP: A Graph Neural Network Accelerator Architecture
Stanford
[paper]
HPCA 2020
HyGCN: A GCN Accelerator with Hybrid Architecture
UCSB
[paper]
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