Scalable Parallel Motion Estimation on Muti-GPU system
- 10.2991/isccca.2013.158How to use a DOI?
- scalable,motion estimation,full search,multi-GPU,
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.
- © 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/).
Cite this article
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 -