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NeuGraph:Parallel Deep Neural Network Computation on Large Graphs
Lingxiao Ma†, Zhi Yang†, Youshan Miao ‡, Jilong Xue ‡, Ming Wu ‡, Lidong Zhou ‡, Yafei Dai†
† Peking University
‡ Microsoft Research
USENIX ATC’19 Lightning Talk, Renton, WA, USA
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs 2
Image Object Detection Speech Recognition
Self-Driving Personal Assistant
User-Item Graph Knowledge Graph
Recommendation Question Answering
Neural Networks
Input Feature Vector
Input: Graph Data
Graph Neural Networks
* Figures from Internet
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs
Graph Neural Networks (GNN)
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- Information propagation via Graph
- Information transformation via Neural Networks
VertexNN Transformation
EdgeNN Transformation
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs
Challenges in Processing GNNs
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large, sparse, and irregular
Scalability ProblemEfficiency Problem
small and regular
Graphs
* Figures from Internet
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs
NeuGraph- Bridge graph and dataflow models to support efficient and scalable GNN processing
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Kernel Optimization
Streaming Optimization
Dataflow Generation
SAGA-NN Abstraction Programming GNN apps
Handle Scalability
Handle Efficiency
Compatibility
W
E0,0V0 V1
V0
SAG SAG
A0 matmul ReLU
ApplyVertex
E1,0weights params.
Existing Dataflow System
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs
NeuGraph- Bridge graph and dataflow models to support efficient and scalable GNN processing
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- Performance- Outperform state-of-the-art frameworks (e.g., TensorFlow and DGL) on small graphs
- Scale to large real-world graphs with GPUs
2019 USENIX Annual Technical ConferenceTrack II: Graph Processing Frameworks
11:55 AM-12:15 PM, on July 11th
NeuGraph