A Scheduling Strategy of Naive Bayesian image classification on HSA Platform

DOI

10.2991/icmeit-17.2017.122How to use a DOI?

Keywords

Heterogeneous computing; HSA; Naive Bayesian image classification; Scheduling Strategy

Abstract

As the heterogeneous system based on CPU-GPU architecture has a strong performance advantages, so that it has been widely used in many fields especially image recognition, in order to enhance the transmission efficiency on HSA (Heterogeneous System Architecture) Platform. In this paper, we design a naive Bayesian image classification algorithm on the APU, which supports the HSA standard, and design a set of resource dynamic scheduling strategy for the eigenvalue extraction of the algorithm. The experimental results show that this strategy can save 29.56% execution time on average.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Nan Xiao
PY  - 2017/05
DA  - 2017/05
TI  - A Scheduling Strategy of Naive Bayesian image classification on HSA Platform
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 670
EP  - 675
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.122
DO  - 10.2991/icmeit-17.2017.122
ID  - Xiao2017/05
ER  -