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 language | English |
|---|---|
| Title of host publication | ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks |
| Publisher | IEEE Computer Society |
| Pages | 240-242 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781509047499 |
| DOIs | |
| Publication status | Published - 2017 Jul 26 |
| Event | 9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 - Milan, Italy Duration: 2017 Jul 4 → 2017 Jul 7 |
Other
| Other | 9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 |
|---|---|
| Country | Italy |
| City | Milan |
| Period | 17/7/4 → 17/7/7 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
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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