Performance of deep learning computation with TensorFlow software library in GPU-capable multi-core computing platforms

Young Jong Mo, Joongheon Kim, Jong-Kook Kim, Aziz Mohaisen, Woojoo Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In this paper we measure and verify the performance improvements in deep learning computation under the support of GPU-enabled multi-core parallel computing platforms. To measure the performance practically, we built our own computing platforms using a GPU hardware (1152 cores) and the TensorFlow software library. In order to evaluate the performance with GPU, we conducted the deep learning computation with various numbers of hidden layers in multilayer perceptron. As presented in the comparative performance results, utilizing GPU hardware improved the performance in terms of computation time (about 3 times or even more).

Original languageEnglish
Title of host publicationICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages240-242
Number of pages3
ISBN (Electronic)9781509047499
DOIs
Publication statusPublished - 2017 Jul 26
Event9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 - Milan, Italy
Duration: 2017 Jul 42017 Jul 7

Other

Other9th International Conference on Ubiquitous and Future Networks, ICUFN 2017
CountryItaly
CityMilan
Period17/7/417/7/7

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Performance of deep learning computation with TensorFlow software library in GPU-capable multi-core computing platforms'. Together they form a unique fingerprint.

  • Cite this

    Mo, Y. J., Kim, J., Kim, J-K., Mohaisen, A., & Lee, W. (2017). Performance of deep learning computation with TensorFlow software library in GPU-capable multi-core computing platforms. In ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks (pp. 240-242). [7993784] IEEE Computer Society. https://doi.org/10.1109/ICUFN.2017.7993784