On Construction of a Caffe Deep Learning Framework based on Intel Xeon Phi View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-17

AUTHORS

Chao-Tung Yang , Jung-Chun Liu , Yu-Wei Chan , Endah Kristiani , Chan-Fu Kuo

ABSTRACT

With the increase of processor computing power, also a substantial rise in the development of many scientific applications, such as weather forecast, financial market analysis, medical technology and so on. The need for more intelligent data increases significantly. Deep Learning as a framework that able to understand the abstract information such as images, text, and sound has a challenging area in recent research works. This phenomenon makes the accuracy and speed are essential for implementing a large neural network. Therefore in this paper, we intend to implement Caffe deep learning framework on Intel Xeon Phi and measure the performance of this environment. In this case, we conduct three experiments. First, we evaluated the accuracy of Caffe deep learning framework in several numbers of iterations on Intel Xeon Phi. For the speed evaluation, in the second experiment we compared the training time before and after optimization on Intel Xeon E5-2650 and Intel Xeon Phi 7210 . In this case, we use vectorization, OpenMP parallel processing, message transfer Interface (MPI) for optimization. In the third experiment, we compared multinode execution results on two nodes of Intel Xeon E5-2650 and two nodes of Intel Xeon Phi 7210. More... »

PAGES

96-106

Book

TITLE

Advances on P2P, Parallel, Grid, Cloud and Internet Computing

ISBN

978-3-030-02606-6
978-3-030-02607-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-02607-3_9

DOI

http://dx.doi.org/10.1007/978-3-030-02607-3_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107669895


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