论文标题
深图库优化英特尔（R）X86体系结构
Deep Graph Library Optimizations for Intel(R) x86 Architecture
论文作者
论文摘要
Deep Graph库（DGL）被设计为通过支持图形的核心抽象（包括流行的图形神经网络（GNN））来启用结构学习的工具。 DGL包含CPU和GPU的所有核心图操作的实现。在本文中，我们专门关注CPU实施和当前的性能分析，使用最新版本的DGL（0.4.3）的一组GNN应用程序的优化和结果。在7个应用程序中，我们实现了基线CPU实施的1.5x-13x的加速度。
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.
