Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation
Through reorganizing the execution order and optimizing the data structure, we proposed an efficient parallel framework for H.264/AVC encoder based on massively parallel architecture. We implemented the proposed framework by CUDA on NVIDIA’s GPU. Not only the compute intensive components of the H.26...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/716020 |
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| _version_ | 1849468513806188544 |
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| author | Huayou Su Mei Wen Nan Wu Ju Ren Chunyuan Zhang |
| author_facet | Huayou Su Mei Wen Nan Wu Ju Ren Chunyuan Zhang |
| author_sort | Huayou Su |
| collection | DOAJ |
| description | Through reorganizing the execution order and optimizing the data structure, we proposed an efficient parallel framework for H.264/AVC encoder based on massively parallel architecture. We implemented the proposed framework by CUDA on NVIDIA’s GPU. Not only the compute intensive components of the H.264 encoder are parallelized but also the control intensive components are realized effectively, such as CAVLC and deblocking filter. In addition, we proposed serial optimization methods, including the multiresolution multiwindow for motion estimation, multilevel parallel strategy to enhance the parallelism of intracoding as much as possible, component-based parallel CAVLC, and direction-priority deblocking filter. More than 96% of workload of H.264 encoder is offloaded to GPU. Experimental results show that the parallel implementation outperforms the serial program by 20 times of speedup ratio and satisfies the requirement of the real-time HD encoding of 30 fps. The loss of PSNR is from 0.14 dB to 0.77 dB, when keeping the same bitrate. Through the analysis to the kernels, we found that speedup ratios of the compute intensive algorithms are proportional with the computation power of the GPU. However, the performance of the control intensive parts (CAVLC) is much related to the memory bandwidth, which gives an insight for new architecture design. |
| format | Article |
| id | doaj-art-fcee0897a6a849e7bc5d2dbb1171cd2f |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-fcee0897a6a849e7bc5d2dbb1171cd2f2025-08-20T03:25:50ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/716020716020Efficient Parallel Video Processing Techniques on GPU: From Framework to ImplementationHuayou Su0Mei Wen1Nan Wu2Ju Ren3Chunyuan Zhang4School of Computer Science and Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaSchool of Computer Science and Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaSchool of Computer Science and Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaSchool of Computer Science and Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaSchool of Computer Science and Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, Hunan 410073, ChinaThrough reorganizing the execution order and optimizing the data structure, we proposed an efficient parallel framework for H.264/AVC encoder based on massively parallel architecture. We implemented the proposed framework by CUDA on NVIDIA’s GPU. Not only the compute intensive components of the H.264 encoder are parallelized but also the control intensive components are realized effectively, such as CAVLC and deblocking filter. In addition, we proposed serial optimization methods, including the multiresolution multiwindow for motion estimation, multilevel parallel strategy to enhance the parallelism of intracoding as much as possible, component-based parallel CAVLC, and direction-priority deblocking filter. More than 96% of workload of H.264 encoder is offloaded to GPU. Experimental results show that the parallel implementation outperforms the serial program by 20 times of speedup ratio and satisfies the requirement of the real-time HD encoding of 30 fps. The loss of PSNR is from 0.14 dB to 0.77 dB, when keeping the same bitrate. Through the analysis to the kernels, we found that speedup ratios of the compute intensive algorithms are proportional with the computation power of the GPU. However, the performance of the control intensive parts (CAVLC) is much related to the memory bandwidth, which gives an insight for new architecture design.http://dx.doi.org/10.1155/2014/716020 |
| spellingShingle | Huayou Su Mei Wen Nan Wu Ju Ren Chunyuan Zhang Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation The Scientific World Journal |
| title | Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation |
| title_full | Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation |
| title_fullStr | Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation |
| title_full_unstemmed | Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation |
| title_short | Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation |
| title_sort | efficient parallel video processing techniques on gpu from framework to implementation |
| url | http://dx.doi.org/10.1155/2014/716020 |
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