Deep Graph Library Optimizations for Intel(R) x86 Architecture
Parallel Computing Lab, Intel Labs, Intel Corporation, Bangalore, India
arXiv:2007.06354 [cs.DC], (13 Jul 2020)
@misc{avancha2020deep,
title={Deep Graph Library Optimizations for Intel(R) x86 Architecture},
author={Sasikanth Avancha and Vasimuddin Md and Sanchit Misra and Ramanarayan Mohanty},
year={2020},
eprint={2007.06354},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
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.
July 19, 2020 by hgpu
