Scalable Parallel Motion Estimation on Muti-GPU system
With NVIDIAs 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.
scalable motion estimation full search multi-GPU
Dong Chen Huayou Su Wen Mei Lixuan Wang Chunyuan Zhang
Computer School National University of Defense Technology ChangSha, China
国际会议
太原
英文
628-632
2013-04-06（万方平台首次上网日期，不代表论文的发表时间）