CAJ | 학술논문

针对并行处理H.264标准视频流解码问题，提出基于CPU/GPU的协同运算算法。以统一设备计算架构(CUDA)语言作为GPU编程模型，实现DCT逆变换与帧内预测在GPU中的加速运算。在保持较高计算精度的前提下，结合CUDA混合编程，提高系统的计算性能。利用NIVIDIA提供的CUDA语言，在解码过程中使DCT逆变换和帧内预测在GPU上并行实现，将并行算法与CPU 单机实现进行比较，并用不同数量的视频流验证并行解码算法的加速效果。实验结果表明，该算法可大幅提高视频流的编解码效率，比CPU单机的平均计算加速比提高10倍。
침대병행처리H.264표준시빈류해마문제，제출기우CPU/GPU적협동운산산법。이통일설비계산가구(CUDA)어언작위GPU편정모형，실현DCT역변환여정내예측재GPU중적가속운산。재보지교고계산정도적전제하，결합CUDA혼합편정，제고계통적계산성능。이용NIVIDIA제공적CUDA어언，재해마과정중사DCT역변환화정내예측재GPU상병행실현，장병행산법여CPU 단궤실현진행비교，병용불동수량적시빈류험증병행해마산법적가속효과。실험결과표명，해산법가대폭제고시빈류적편해마효솔，비CPU단궤적평균계산가속비제고10배。
In terms of parallel decoding H.264 video stream problems, this paper builds CPU/GPU cooperative computing model to accelerate video encoding and decoding computing. This model uses Compute Unified Device Architecture(CUDA) language as GPU programming model, proposes and implements DCT inverse conversation and intra-frame prediction in a GPU accelerated computing. In the premise of maintaining higher calculation accuracy, combined with CUDA mixed programming, improves the computational performance of the system greatly. The algorithm uses CUDA language provided by NVIDIA, and realizes the DCT inverse conversation and intra-frame prediction on GPU. The experiment compares the parallel algorithm and the sole CPU, and verifies the accelerating effect of the parallel decoding algorithm by using different number of video streams. Experimental result shows that this system improves the video streaming codec efficiency, and it can accelerate 10 times faster than the average CPU sole calculation.