Power and Performance Analysis of Motion Estimation Based on Hardware and Software Realizations
IEEE Transactions on Computers
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Tips, tricks and troubles: optimizing for cell and GPU
Proceedings of the 20th international workshop on Network and operating systems support for digital audio and video
Parallel programming for multimedia applications
Multimedia Tools and Applications
Image and video processing on CUDA: state of the art and future directions
MACMESE'11 Proceedings of the 13th WSEAS international conference on Mathematical and computational methods in science and engineering
Parallel design for error-resilient entropy coding algorithm on GPU
Journal of Parallel and Distributed Computing
Parallelization of Full Search Motion Estimation Algorithm for Parallel and Distributed Platforms
International Journal of Parallel Programming
| Hi-index | 0.00 |
Today, world is rapidly turning to high definition multimedia. From engineering and programming point of view, this usually means more computation is needed and more memory space is required to achieve these higher qualities. In this paper we explore the use of parallelization opportunities in graphics processors to accelerate video encoding. We evaluate the NVIDIA CUDA[1] toolkit and evaluate the performance of motion estimation in video encoding. The main goal of this paper is to evaluate the capabilities of NVIDIA/CUDA and develop a process for implementing video/multimedia applications. We have discovered that the difference in performance when CUDA is not used properly can be over 100x. We show how we were able to use CUDA capabilities to reduce the motion estimation time from 7000 milli seconds to 70 milli seconds.