HyTruongSon / GraphFlow
Deep Learning framework in C++/CUDA that supports symbolic/automatic differentiation, dynamic computation graphs, tensor/matrix operations accelerated by GPU and implementations of various state-of-the-art graph neural networks and other Machine Learning models including Covariant Compositional Networks For Learning Graphs [Risi et al]
☆53Updated 3 years ago
Alternatives and similar repositories for GraphFlow:
Users that are interested in GraphFlow are comparing it to the libraries listed below
- ☆36Updated 10 months ago
- Light-weight GPU kernel interface for graph operations☆15Updated 4 years ago
- The pMMF Multiresolution Matrix Factorization Library☆26Updated 6 years ago
- A Distributed Multi-GPU System for Fast Graph Processing☆65Updated 6 years ago
- G3: A Programmable GNN Training System on GPU☆43Updated 4 years ago
- Asynchronous Multi-GPU Programming Framework☆46Updated 3 years ago
- Parallel Tensor Infrastructure (ParTI!)☆28Updated 4 years ago
- The official SALIENT system described in the paper "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and P…☆39Updated last year
- Low-Level Graph Neural Network Operators for PyG☆187Updated this week
- ☆71Updated 3 years ago
- Deriving Neural Architectures from Sequence and Graph Kernels☆59Updated 7 years ago
- Source code for our AAAI paper "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks".☆187Updated 3 years ago
- Sparse matrix computation library for GPU☆54Updated 4 years ago
- Experiments with Message Passing GNNs in C++ and PyTorch.☆24Updated 8 months ago
- Implementation of the Multiscale Laplacian Graph Kernel☆18Updated 5 years ago
- HogWild++: A New Mechanism for Decentralized Asynchronous Stochastic Gradient Descent☆33Updated 8 years ago
- Notebooks for IPAM Tutorial, March 15 2019☆24Updated 6 years ago
- CUDA Matrix Factorization Library with Stochastic Gradient Descent (SGD)☆71Updated 7 years ago
- ☆45Updated 2 weeks ago
- Slides and notebooks for the IfI Summer School 2018 on Machine Learning☆36Updated 6 years ago
- Code for paper "Design Principles for Sparse Matrix Multiplication on the GPU" accepted to Euro-Par 2018☆71Updated 4 years ago
- "Do We Need Anisotropic Graph Neural Networks?" at ICLR 2022☆32Updated 3 years ago
- BayesGrad: Explaining Predictions of Graph Convolutional Networks☆63Updated 3 years ago
- Graph Challenge☆31Updated 5 years ago
- ☆12Updated 2 years ago
- ☆91Updated 8 years ago
- Deep learning with a multiplication budget☆47Updated 6 years ago
- Implements the frequent directions algorithm for approximating matrices in streams☆32Updated 7 years ago
- Graphiler is a compiler stack built on top of DGL and TorchScript which compiles GNNs defined using user-defined functions (UDFs) into ef…☆61Updated 2 years ago
- The Surprisingly ParalleL spArse Tensor Toolkit.☆70Updated 3 years ago