Performance modelling of deep learning on intel many integrated core architectures
Authors:
- Andre Viebke,
- Sabri Pllana,
- Suejb Memeti,
- Joanna Kolodziej
Abstract
Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requires parallel computing resources. In this paper, we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture. While for the first performance model we minimally use measurement techniques for parameter value estimation, in the second model we estimate more parameters based on measurements. We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi. The achieved average performance prediction accuracy is about 15% for the first model and 11% for second model.
- Record ID
- CUT4a91f6be09394e3b8ca75bbd85211058
- Publication categories
- ; ;
- Author
- Pages
- 724-731
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 731; Bibliografia (liczba pozycji) - 37; Oznaczenie streszczenia - Abstr.
- Substantive notes
- Punktacja MNiSW/MEiN (rozdział) - 20
- Book
- 2019 International Conference on High Performance Computing & Simulation (HPCS), 2019, [S.l.], Institute of Electrical and Electronics Engineers, IEEE, ISBN 978-1-7281-4484-9 (electronic)
- Keywords in English
- deep learning, Convolutional Neural Network (CNN), performance modelling, Intel Many Integrated Core (MIC) Architecture, Intel Xeon Phi
- DOI
- DOI:10.1109/HPCS48598.2019.9188090 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/9188090 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 70
- Additional fields
- Indeksowana w: Web of Science, Scopus, CORE
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT4a91f6be09394e3b8ca75bbd85211058/
- URN
urn:pkr-prod:CUT4a91f6be09394e3b8ca75bbd85211058
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.