Performance Prediction of GPU-based Deep Learning Applications

Eugenio Gianniti, Li Zhang, Danilo Ardagna

2019

Abstract

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, discuss, and validate a black box approach to model the execution time for training convolutional neural networks (CNNs), with a particular focus on deployments on general purpose graphics processing units (GPGPUs). We demonstrate that our approach is generally applicable to a variety of CNN models and different types of GPGPUs with high accuracy. The proposed method can support with great precision (within 5% average percentage error) the management of production environments.

Paper Citation

in Harvard Style

Gianniti E., Zhang L. and Ardagna D. (2019). Performance Prediction of GPU-based Deep Learning Applications.In Proceedings of the 9th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-365-0, pages 279-286. DOI: 10.5220/0007681802790286

in Bibtex Style

@conference{closer19,
author={Eugenio Gianniti and Li Zhang and Danilo Ardagna},
title={Performance Prediction of GPU-based Deep Learning Applications},
booktitle={Proceedings of the 9th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2019},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007681802790286},
isbn={978-989-758-365-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Performance Prediction of GPU-based Deep Learning Applications
SN - 978-989-758-365-0
AU - Gianniti E.
AU - Zhang L.
AU - Ardagna D.
PY - 2019
SP - 279
EP - 286
DO - 10.5220/0007681802790286