HTF‐MPR: A Heterogeneous TensorFlow Mapper Targeting Performance using Genetic Algorithms and Gradient Boosting Regressors
Ahmad Albaqsamia, Maryam S. Hosseinib and Nader Bagherzadeh c
Department of Electrical Engineering and Computer Science, University of California, Irvine, CA, USA
aaalbaqsa@uci.edu
bmseyyedh@uci.edu
cnader@uci.edu
ABSTRACT
TensorFlow [1] is a library developed by Google to implement Artificial Neural Networks using computational dataflow graphs. The neural network has many iterations during training. A distributed, parallel environment is ideal to speedup learning. Parallelism requires proper mapping of devices to TensorFlow operations. We developed HTF‐MPR framework for that reason. HTF‐MPR utilizes a genetic algorithm approach to search for the best mapping that outperforms the default Tensorflow mapper. By using Gradient Boosting Regressors to create the fitness predictive model, the search space is expanded which increases the chances of finding a solution mapping. Our results on well‐known neural network benchmarks, such as ALEXNET, MNIST softmax classifier, and VGG‐16, show an overall speedup in the training stage by 1.18, 3.33, and 1.13, respectively.
