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Deep Graph Library Optimizations for Intel(R) x86 Architecture

Distributed, Parallel, and Cluster Computing 2020-07-14 v1 Machine Learning
Authors: Sasikanth Avancha , Vasimuddin Md , Sanchit Misra , Ramanarayan Mohanty
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Abstract

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.

Keywords

graph neural network deep neural network acceleration gpu computing

Cite

@article{arxiv.2007.06354,
  title  = {Deep Graph Library Optimizations for Intel(R) x86 Architecture},
  author = {Sasikanth Avancha and Vasimuddin Md and Sanchit Misra and Ramanarayan Mohanty},
  journal= {arXiv preprint arXiv:2007.06354},
  year   = {2020}
}

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