22267

Deep Graph Library Optimizations for Intel(R) x86 Architecture

Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty
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}

}

Download Download (PDF)   View View   Source Source   Source codes Source codes

394

views

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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2020 hgpu.org

All rights belong to the respective authors

Contact us: