Performance Prediction of GPU-Based Deep Learning Applications

• Eugenio Gianniti Politecnico di Milano
• Li Zhang IBM T. J. Watson Research Center
• Danilo Ardagna Politecnico di Milano

Resumo

Recent years saw an increasing success in the application of deep learning methods across various domains and for tackling different problems, ranging from image recognition and classification to text processing and speech recognition. In this paper we propose and validate an approach to model the execution time for training convolutional neural networks (CNNs) deployed on GPGPUs. We demonstrate that our approach is generally applicable to a variety of CNN models and different types of G PG PU s with high accuracy, aiming at the preliminary design phases for system sizing.

Palavras-chave: Training, Computational modeling, Computational complexity, Linear regression, Backpropagation, Convolutional neural networks, deep learning, performance prediction, general purpose GPUs