This is the repo to collect latest materials of GNN, mainly focus on system context. Welcome to contribute!
- [KDD'22] Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs [Paper]
- [MLSys'22] Sequential aggregation and rematerialization: Distributed full-batch training of graph neural networks on large graphs [Paper]
- [KDD'21] Global Neighbor Sampling for Mixed CPU-GPU Training onGiant Graphs [Paper]
- [OSDI'21] P3: Distributed Deep Graph Learning at Scale [Paper]
- [OSDI'21] Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads [Paper]
- [NSDI'21] GAIA: A System for Interactive Analysis on Distributed Graphs Using a High-Level Language [Paper]
- [EuroSys'21] DGCL: An Efficient Communication Library for Distributed GNN Training [Paper]
- [EuroSys'21] FlexGraph: a flexible and efficient distributed framework for GNN training [Paper]
- [IA3'20] Distdgl: distributed graph neural network training for billion-scale graphs [Paper]
- [SOSP'19] Knightking: a fast distributed graph random walk engine [Paper]
- [VLDB'22] Marius++: Large-Scale Training of Graph Neural Networks on aSingle Machine [Paper] [Website] [Repo]
- [MLSys'22] Accelerating training and inference of graph neural networks with fast sampling and pipelining [Paper]
- [PPoPP'22] Accelerating Quantized Graph Neural Networks via GPU Tensor Core [Papers]
- [PPoPP'21] Understanding and Bridging the Gaps in Current GNN Performance Optimizations [Paper]
- [OSDI'21] GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs [Paper] [Presentation] [Repo] [Chinese Blog from ZobinHuang]
- [OSDI'21] Marius: Learning Massive Graph Embeddings on a Single Machine [Paper] [Website] [Repo]
- [VLDB'21] Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture [Paper]
- [EuroSys'21] Accelerating graph sampling for graph machine learning using GPUs [Paper] [Repo]
- [EuroSys'21] Seastar: vertex-centric programming for graph neural networks [Paper]
- [MLSys'20] Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc [Paper]
- [VLDB'20] G3: When Graph Neural Networks Meet Parallel GraphProcessing Systems on GPUs [Paper]
- [MLSys'19] Optimizing DNN computation with relaxed graph substitutions [Paper]
- [ATC'19] NeuGraph: Parallel Deep Neural Network Computation on Large Graphs [Paper] [Presentation] [Chinese Blog from ZobinHuang]
- [WWW'19] GraphVite: A High-Performance CPU-GPU Hybrid System forNode Embedding [Paper]
- [Unknown] PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses [Paper]
- [PPoPP'22] Scaling Graph Traversal to 281 Trillion Edges with 40 Million Cores [Paper]
- [EuroSys'21] Tripoline: generalized incremental graph processing via graph triangle inequality [Paper]
- [EuroSys'21] DZiG: Sparsity-Aware Incremental Processing of Streaming Graphs [Paper]
- [EuroSys'20] Subway: Minimizing Data Transfer duringOut-of-GPU-Memory Graph Processing [Paper]
- [ATC'19] SIMD-X: Programming and Processing of Graph Algorithms on GPUs [Paper] [Repo]
- [ASPLOS'18] Tigr: Transforming Irregular Graphs forGPU-Friendly Graph Processing [Paper] [Repo]
- [PPoPP'16] Gunrock: A High-Performance Graph Processing Library on the GPU [Paper] [Repo]
- [SC'15] Enterprise: Breadth-First Graph Traversal on GPUs [Paper] [Repo]
- [HPDC'14] Cusha: vertex-centric graph pro-cessing on gpus [Paper] [Repo]
- [OSDI'12] PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs [Paper]
- [SIGMOD'10] Pregel: A System for Large-Scale Graph Processing [Paper]
- [EuroSys'21] Tesseract: distributed, general graph pattern mining on evolving graphs [Paper]
- [VLDB'20] Pangolin: An Efficient and Flexible Graph Mining Systemon CPU and GPU [Paper]
- [SOSP'19] AutoMine: Harmonizing High-Level Abstraction and High Performance for Graph Mining [Paper]
- [OSDI'18] ASAP: Fast, Approximate Graph Pattern Mining at Scale [Paper]
- [SOSP'15] Arabesque: A System for Distributed Graph Mining [Paper]
- Deep Graph Library (DGL) [Website] [Paper] [Repo]
- PyG (PyTorch Geometric) [Repo] [Paper]
- Cogdl [Repo] [Paper]
- Graph-Learn [Repo] [Paper]
- PyTorch-BigGraph [Repo] [Paper]
- Gunrock [Repo] [Paper]
- SNAP (Stanford Network Analysis Platform) [Website] [Repo]
- Open Graph Benchmark [Website]