Scalable Parallel Motion Estimation on Muti-GPU system

DOI

10.2991/isccca.2013.158How to use a DOI?

Keywords

scalable,motion estimation,full search,multi-GPU,

Abstract

With NVIDIA’s parallel computing architecture CUDA, using GPU to speed up compute-intensive applications has become a research focus in recent years. In this paper, we proposed a scalable method for multi-GPU system to accelerate motion estimation algorithm, which is the most time consuming process in video encoding. Based on the analysis of data dependency and multi-GPU architecture, a parallel computing model and a communication model are designed. We tested our parallel algorithm and analyzed the performance with 10 standard video sequences in different resolutions using 4 NVIDIA GTX460 GPUs, and calculated the overall speedup. Our results show that a speedup of 36.1 times using 1 GPU and more than 120 times for 4 GPUs on 1920x1080 sequences. Further, our parallel algorithm demonstrated the potential of nearly linear speedup according to the number of GPUs in the system.

Copyright

© 2013, 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/).

Download article (PDF)

TY  - CONF
AU  - Dong Chen
AU  - Huayou Su
AU  - Wen Mei
AU  - Lixuan Wang
AU  - Chunyuan Zhang
PY  - 2013/02
DA  - 2013/02
TI  - Scalable Parallel Motion Estimation on Muti-GPU system
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
PB  - Atlantis Press
SP  - 628
EP  - 632
SN  - 1951-6851
UR  - https://doi.org/10.2991/isccca.2013.158
DO  - 10.2991/isccca.2013.158
ID  - Chen2013/02
ER  -