# Deep Graph Library Optimizations for Intel(R) x86 Architecture

arXiv: https://arxiv.org/abs/2007.06354 · HTML: https://mlsanity.com/paper/2007.06354/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks](https://mlsanity.com/paper/2104.06700/) (2021)
- [The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science](https://mlsanity.com/paper/2210.17484/) (2022)
- [Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads](https://mlsanity.com/paper/2104.05755/) (2021)

## 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.