Deep Graph Library Optimizations for Intel(R) x86 Architecture
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Abstract (arXiv)
The Deep Graph Library (DGL) was designed as a tool to enable structure learning from graphs, by supporting a core abstraction for graphs, including the popular Graph Neural Networks (GNN). DGL contains implementations of all core graph operations for both the CPU and GPU. In this paper, we focus specifically on CPU implementations and present performance analysis, optimizations and results across a set of GNN applications using the latest version of DGL(0.4.3). Across 7 applications, we achieve speed-ups ranging from1 1.5x-13x over the baseline CPU implementations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
- 2022The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Sciencecited by 14
- 2021Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloadscited by 8
- 2022The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science31 Oct 2022
- 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks14 Apr 2021
- 2021Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads12 Apr 2021