The performance analysis of model parallelism in deep residual network

Yuqin Feng

doi:10.1117/12.2655979

12 January 2023 The performance analysis of model parallelism in deep residual network

Yuqin Feng

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125091J (2023) https://doi.org/10.1117/12.2655979
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

With the rapid information and communications technology growth and continuous invention, the concept of parallel computing has become the core of computer science, and its capabilities are continually documented in providing high processing solutions. The graphic processing unit (GPU) plays a crucial role in deep learning and parallel computing. The primary purpose of this paper is to discuss two concepts of deep computing: data parallelism and model parallelism. Additionally, the discussion will delve into their importance and associated challenges in parallel processing. Furthermore the paper summarises the Residual Network (ResNet) system with the basic principles and formulas. Establishing efficient parallel algorithms for GPU processing is necessary. This paper evaluates the performance and speed between single-core and multi-core working environments for two kinds of convolutional neural network structures with different types of GPUs. The first one is LeNet, and the other one is VGG16. There are two final results of this experiment. By analyzing the data of performance, VGG16 has better performance than LeNet. By analyzing the data of speed, the speed of LeNet is faster than that of VGG16.

Citation Download Citation

Yuqin Feng "The performance analysis of model parallelism in deep residual network", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125091J (12 January 2023); https://doi.org/10.1117/12.2655979

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Parallel computing

Distributed computing

Neural networks

Performance modeling

Evolutionary algorithms

Graphics processing units

Show All Keywords

Keywords/Phrases

Search In:

Publication Years