| GPU acceleration of predictive partitioned vector quantization for ultraspectral sounder data compression | |
|---|---|
| 學年 | 100 |
| 學期 | 1 |
| 出版（發表）日期 | 2011-09-01 |
| 作品名稱 | GPU acceleration of predictive partitioned vector quantization for ultraspectral sounder data compression |
| 作品名稱（其他語言） | |
| 著者 | Wei, Shih-chieh; Huang, Bormin |
| 單位 | 淡江大學資訊管理學系 |
| 出版者 | Piscataway: Institute of Electrical and Electronics Engineers |
| 著錄名稱、卷期、頁數 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4(3), pp.677-682 |
| 摘要 | 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. |
| 關鍵字 | Graphics processing unit;Vector quantization;Training;Instruction sets;Vectors;Kernel;Pixel |
| 語言 | en_US |
| ISSN | 1939-1404 2151-1535 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | Huang, Bormin |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/62273 ) |
| SDGS | 優質教育,氣候行動 |
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