Performance Analysis of Parallel and Scalable GPU Based Convolutional Neural Network

  • Umesh ChavanEmail author
  • Dinesh Kulkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1025)


Convolutional Neural networks (CNN) have succeeded great impact in various tasks of machine learning. Training CNN model is a computationally intensive task. Scalability and performance of CNN with GPU is demonstrated in this study using CUDA (Compute Unified Device Architecture) framework. We evaluated performance characteristics using our own designed CNN model. The model is configured for Facial expression Recognition classification task. The novelty of this effort is to demonstrate performance acceleration in scalable CNN. The parallel tasks using the hardware feature of general-purpose- computing on graphics processing unit (GPGPU) has been shown to be appropriate to be applied to CNN. We implemented at multi-node distributed training; which allows us to efficiently parallelize deep networks across multiple servers, in order to minimize the time to train. The experiment result shows that the proposed experiment in this study gained over 7 times speedup.


Deep learning CNN Graphical processing unit 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Walchand College of EngineeringSangliIndia

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