检索结果-内蒙古大学图书馆

GPU Acceleration of predictive partitioned vector quantization for Ultraspectral Sounder Data Compression

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2011年第3期4卷 677-682页

作者： Wei, Shih-Chieh Huang, Bormin Univ Wisconsin Space Sci & Engn Ctr Madison WI 53706 USA

For the large-volume ultraspectral sounder data, compression is desirable to save storage space and transmission time. To retrieve the geophysical paramters without losing precision the ultraspectral sounder data compression has to be lossless. Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by using GPU. predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit depth partitioning, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a spatial division design shows a speedup of 72x in our four-GPU-based implementation of the PPVQ compression scheme.

关键词： Graphic processor unit lossless data compression predictive partitioned vector quantization ultraspectral sounder data

来源：评论

学校读者我要写书评

暂无评论

A GPU-Based Implementation of predictive partitioned vector quantization for Compression of Ultraspectral Sounder Data

A GPU-Based Implementation of Predictive Partitioned Vector ...

引用

Conference on Satellite Data Compression, Communications, and Processing VI

作者： Wei, Shih-Chieh Huang, Bormin Tamkang Univ Dept Informat Management Tamsui 25137 Taiwan Univ Wisconsin CIMSS SSEC Madison WI 53706 USA

ISBN: (纸本)9780819483065

Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by a GPU-based implementation. For the voluminous ultraspectral sounder data, lossless compression is desirable to save storage space and transmission time without losing precision in retrieval of geophysical parameters. predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit partition, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a speedup of 42x has been achieved in our GPU-based implementation of the PPVQ compression scheme.

关键词： graphic processor unit predictive partitioned vector quantization lossless data compression

来源：评论

学校读者我要写书评

暂无评论

Further GPU Acceleration of predictive partitioned vector quantization for Ultraspectral Sounder Data Compression

Further GPU Acceleration of Predictive Partitioned Vector Qu...

引用

Conference on Satellite Data Compression, Communications, and Processing VII

作者： Wei, Shih-Chieh Huang, Bormin Tamkang Univ Dept Informat Management Tamsui 25137 Taiwan Univ Wisconsin SSEC CIMSS Madison WI 53706 USA

ISBN: (纸本)9780819487674

For the ultraspectral sounder data which features thousands of channels at each observation location, lossless compression is desirable to save storage space and transmission time without losing precision in retrieval of geophysical parameters. predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit-depth partitioning, vector quantization, and entropy coding. In our previous work, the two most time consuming stages of linear prediction and vector quantization were identified for GPU implementation. For GIFTS data, using a spectral division strategy for sharing the compression workload among four GPUs, a speedup of similar to 42x was achieved. To further enhance the speedup, this work will explore a spatial division strategy for sharing workload in processing the six parts of a GIFTS datacube. As result, the total processing time of a GIFTS datacube on four GPUs can be less than 13 seconds which is equivalent to a speedup of similar to 72x. The use of multiple GPUs for PPVQ compression is thus promising as a low-cost and effective compression solution for ultraspectral sounder data for rebroadcast use.

关键词： graphic processor unit predictive partitioned vector quantization lossless data compression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：