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A GPU vs CPU performance evaluation of an experimental video compression algorithm

机译:实验视频压缩算法的GPU与CPU性能评估

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Modern video compression algorithms put significant strain on a system's CPU, especially for video encoding. The ever increasing demands for using video compression algorithms in a wide range of applications necessitate the use of processing components that boost the speed and quality of the video compression algorithm's execution. The vast parallel computational capabilities of modern graphics processing units (GPUs) that usually remain underutilized makes them suitable for handling the processing load for video coding. This paper examines and evaluates the performance benefits of using the GPU over the CPU for an experimental video compression algorithm. An NVIDIA CUDA GPU implementation is evaluated against a traditional multithreaded CPU implementation. Experimental results show that at the highest resolution examined, the GPU approach achieved an impressive speedup ratio of 21.303x against the CPU for the decoding process, while for encoding, the speedup ratio reached 11.048x. Overall results indicate the prevalence of the GPU over the CPU, justified reasonably by the massive parallelism offered by the GPGPU computing paradigm, showing that the GPU should be the architecture of choice for high definition video coding.
机译:现代视频压缩算法给系统的CPU带来了很大的压力,尤其是对于视频编码。对于在广泛的应用中使用视频压缩算法的不断增长的需求,使得必须使用能够提高视频压缩算法的执行速度和质量的处理组件。通常仍未得到充分利用的现代图形处理单元(GPU)巨大的并行计算能力使其适合处理视频编码的处理负载。本文研究并评估了使用GPU优于CPU进行实验性视频压缩算法的性能优势。 NVIDIA CUDA GPU实施是针对传统的多线程CPU实施进行评估的。实验结果表明,在所检查的最高分辨率下,GPU方法在解码过程中相对于CPU达到了令人印象深刻的21.303x的加速比,而在编码方面,加速比达到了11.048x。总体结果表明,GPU在CPU上的普及率可以由GPGPU计算范例提供的大规模并行性合理地证明,这表明GPU应该是高清视频编码的首选架构。

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