Artifact for accepted OSDI'23 paper, Yuke Wang, et al. MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms. OSDI'23
Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. ...
Automatic classification becomes more and more in- teresting as the amount of available data keeps g...
We organize the artifacts according to the appearance order of figures and tables in the paper. NPU ...
Artifact for accepted OSDI'23 paper, Yuke Wang, et al. MGG: Accelerating Graph Neural Networks with ...
This archive includes source codes and benchmarks for paper: "G-Sparse: Compiler-Driven Acceleration...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
The Graphics Processing Unit (GPU) parallel architecture is now being used not just for graphics but...
Software artifact of the paper "High-Performance and Programmable Attentional Graph Neural Networks ...
Artifact for USENIX ATC'23: TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
A Factor Graph is a bipartite probabilistic graphical model representing the factorization of a func...
Graph convolutional neural networks (GCNs) have emerged as a key technology in various application d...
Simulating biological neural networks is an important task for computational neuroscientists attempt...
Graphics processing units (GPUs) contain a significant number of cores relative to central processin...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, e...
Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. ...
Automatic classification becomes more and more in- teresting as the amount of available data keeps g...
We organize the artifacts according to the appearance order of figures and tables in the paper. NPU ...
Artifact for accepted OSDI'23 paper, Yuke Wang, et al. MGG: Accelerating Graph Neural Networks with ...
This archive includes source codes and benchmarks for paper: "G-Sparse: Compiler-Driven Acceleration...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
The Graphics Processing Unit (GPU) parallel architecture is now being used not just for graphics but...
Software artifact of the paper "High-Performance and Programmable Attentional Graph Neural Networks ...
Artifact for USENIX ATC'23: TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
A Factor Graph is a bipartite probabilistic graphical model representing the factorization of a func...
Graph convolutional neural networks (GCNs) have emerged as a key technology in various application d...
Simulating biological neural networks is an important task for computational neuroscientists attempt...
Graphics processing units (GPUs) contain a significant number of cores relative to central processin...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, e...
Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. ...
Automatic classification becomes more and more in- teresting as the amount of available data keeps g...
We organize the artifacts according to the appearance order of figures and tables in the paper. NPU ...
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