{"title":"并行运动JPEG 2000与CUDA","authors":"S. Datla, Naga Sathish Gidijala","doi":"10.1109/ICCEE.2009.277","DOIUrl":null,"url":null,"abstract":"Due to the rapid growth of Graphics Processing Unit (GPU) processing capability, using GPU as a coprocessor for assisting the CPU in computing massive data has become indispensable. Nvidia’s CUDA general-purpose graphical processing unit (GPGPU) architecture can greatly benefit single instruction multiple thread (SIMT) styled, computationally expensive programs. Video encoding, to an extent, is an excellent example of such an application which can see impressive performance gains from CUDA optimization. This paper details the experience of porting the motion JPEG 2000 reference encoder to the CUDA architecture. Each major structural/computational unit of JPEG 2000 is discussed in the CUDA framework and the results are provided wherever required. Our experimental results demonstrate that the CUDA based implementation works 20.7 times faster than the original implementation on the CPU.","PeriodicalId":343870,"journal":{"name":"2009 Second International Conference on Computer and Electrical Engineering","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Parallelizing Motion JPEG 2000 with CUDA\",\"authors\":\"S. Datla, Naga Sathish Gidijala\",\"doi\":\"10.1109/ICCEE.2009.277\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the rapid growth of Graphics Processing Unit (GPU) processing capability, using GPU as a coprocessor for assisting the CPU in computing massive data has become indispensable. Nvidia’s CUDA general-purpose graphical processing unit (GPGPU) architecture can greatly benefit single instruction multiple thread (SIMT) styled, computationally expensive programs. Video encoding, to an extent, is an excellent example of such an application which can see impressive performance gains from CUDA optimization. This paper details the experience of porting the motion JPEG 2000 reference encoder to the CUDA architecture. Each major structural/computational unit of JPEG 2000 is discussed in the CUDA framework and the results are provided wherever required. Our experimental results demonstrate that the CUDA based implementation works 20.7 times faster than the original implementation on the CPU.\",\"PeriodicalId\":343870,\"journal\":{\"name\":\"2009 Second International Conference on Computer and Electrical Engineering\",\"volume\":\"115 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Second International Conference on Computer and Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEE.2009.277\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Second International Conference on Computer and Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEE.2009.277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
摘要
随着图形处理单元(Graphics Processing Unit, GPU)处理能力的快速增长,使用GPU作为协处理器来辅助CPU进行海量数据的计算已经变得必不可少。英伟达的CUDA通用图形处理单元(GPGPU)架构可以极大地有利于单指令多线程(SIMT)风格的计算昂贵的程序。视频编码,在某种程度上,是这样一个应用程序的一个很好的例子,可以看到令人印象深刻的性能提升从CUDA优化。本文详细介绍了将运动JPEG 2000参考编码器移植到CUDA架构的经验。在CUDA框架中讨论了JPEG 2000的每个主要结构/计算单元,并在需要的地方提供了结果。我们的实验结果表明,基于CUDA的实现比CPU上的原始实现快20.7倍。
Due to the rapid growth of Graphics Processing Unit (GPU) processing capability, using GPU as a coprocessor for assisting the CPU in computing massive data has become indispensable. Nvidia’s CUDA general-purpose graphical processing unit (GPGPU) architecture can greatly benefit single instruction multiple thread (SIMT) styled, computationally expensive programs. Video encoding, to an extent, is an excellent example of such an application which can see impressive performance gains from CUDA optimization. This paper details the experience of porting the motion JPEG 2000 reference encoder to the CUDA architecture. Each major structural/computational unit of JPEG 2000 is discussed in the CUDA framework and the results are provided wherever required. Our experimental results demonstrate that the CUDA based implementation works 20.7 times faster than the original implementation on the CPU.