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 · 2017
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).