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 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
Cite this
Performance of deep learning computation with TensorFlow software library in GPU-capable multi-core computing platforms. / Mo, Young Jong; Kim, Joongheon; Kim, Jong-Kook; Mohaisen, Aziz; Lee, Woojoo.
ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2017. p. 240-242 7993784.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Performance of deep learning computation with TensorFlow software library in GPU-capable multi-core computing platforms
AU - Mo, Young Jong
AU - Kim, Joongheon
AU - Kim, Jong-Kook
AU - Mohaisen, Aziz
AU - Lee, Woojoo
PY - 2017/7/26
Y1 - 2017/7/26
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85028075671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028075671&partnerID=8YFLogxK
U2 - 10.1109/ICUFN.2017.7993784
DO - 10.1109/ICUFN.2017.7993784
M3 - Conference contribution
SP - 240
EP - 242
BT - ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
ER -