Deep Graph Library Optimizations for Intel(R) x86 Architecture

• URL: http://arxiv.org/abs/2007.06354v1
• Date: Mon, 13 Jul 2020 12:57:16 GMT
• Title: Deep Graph Library Optimizations for Intel(R) x86 Architecture
• Authors: Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty
• Abstract summary: We present performance analysis, optimizations and results across a set of Graph Neural Networks (GNN) applications using the latest version of Deep Graph Library (DGL) Across 7 applications, we achieve speed-ups ranging from1 1.5x-13x over the baseline CPU implementations.
• Score: 3.518762870118332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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