基于CUDA的航空γ能谱数据小波降噪并行加速算法
TL84; 航空γ能谱数据体量巨大,使用中央处理器(Central Processing Unit,CPU)进行数据后处理时计算效率低.提出了一种基于通用并行计算架构CUDA(Compute Unified Device Architecture)的图形处理器(Graphics Processing Unit,GPU)并行方案,对航空γ能谱数据小波降噪处理过程进行并行加速优化.首先测试不同block尺寸对计算时间的影响,寻找处理航空γ能谱数据的最佳block尺寸;其次分别使用GPU计算不同数据体量和同一数据体量下不同小波基函数相较于CPU处理航空γ能谱数据的加速比;最后通过对实测航空γ能谱数据...
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| Published in | 核技术 Vol. 47; no. 4; pp. 21 - 31 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
东华理工大学 核科学与工程学院 南昌 330013%苏州大学 医学部放射医学与防护学院 苏州 215123
01.04.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-3219 |
| DOI | 10.11889/j.0253-3219.2024.hjs.47.040201 |
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| Abstract | TL84; 航空γ能谱数据体量巨大,使用中央处理器(Central Processing Unit,CPU)进行数据后处理时计算效率低.提出了一种基于通用并行计算架构CUDA(Compute Unified Device Architecture)的图形处理器(Graphics Processing Unit,GPU)并行方案,对航空γ能谱数据小波降噪处理过程进行并行加速优化.首先测试不同block尺寸对计算时间的影响,寻找处理航空γ能谱数据的最佳block尺寸;其次分别使用GPU计算不同数据体量和同一数据体量下不同小波基函数相较于CPU处理航空γ能谱数据的加速比;最后通过对实测航空γ能谱数据添加白噪声,计算降噪后数据信噪比,优选出适用于GPU并行加速计算的阈值降噪方法.计算结果表明:航空γ能谱数据降噪最佳block二维尺寸在64×64到128×128之间;数据降噪总时间加速比达100倍以上的小波基函数占80%,加速比达90倍以上的小波基函数占91%,其中coif5小波基函数可达353倍,阈值降噪函数加速比可接近570倍.不同降噪函数处理航空γ能谱数据的结果表明,所有函数在低信噪比条件下降噪效果不足,而在高信噪比下则会出现过度降噪,采用硬阈值的coif5、软阈值的coif1和改进阈值的bior3.7小波基函数可得到显著的降噪效果. |
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| AbstractList | TL84; 航空γ能谱数据体量巨大,使用中央处理器(Central Processing Unit,CPU)进行数据后处理时计算效率低.提出了一种基于通用并行计算架构CUDA(Compute Unified Device Architecture)的图形处理器(Graphics Processing Unit,GPU)并行方案,对航空γ能谱数据小波降噪处理过程进行并行加速优化.首先测试不同block尺寸对计算时间的影响,寻找处理航空γ能谱数据的最佳block尺寸;其次分别使用GPU计算不同数据体量和同一数据体量下不同小波基函数相较于CPU处理航空γ能谱数据的加速比;最后通过对实测航空γ能谱数据添加白噪声,计算降噪后数据信噪比,优选出适用于GPU并行加速计算的阈值降噪方法.计算结果表明:航空γ能谱数据降噪最佳block二维尺寸在64×64到128×128之间;数据降噪总时间加速比达100倍以上的小波基函数占80%,加速比达90倍以上的小波基函数占91%,其中coif5小波基函数可达353倍,阈值降噪函数加速比可接近570倍.不同降噪函数处理航空γ能谱数据的结果表明,所有函数在低信噪比条件下降噪效果不足,而在高信噪比下则会出现过度降噪,采用硬阈值的coif5、软阈值的coif1和改进阈值的bior3.7小波基函数可得到显著的降噪效果. |
| Abstract_FL | [Background]The volume of aviation gamma spectrum data is immense.If only a central processing unit(CPU)is used for data post-processing,it would be constrained by computational efficiency.[Purpose]This study aims to propose a CUDA-based graphics processing unit(GPU)parallel solution that optimally accelerates the denoising of airborne gamma-ray spectral data using wavelet transformation.[Methods]First,the impact of different block sizes on computational time was tested to determine the optimal block size for processing airborne gamma-ray spectral data.Subsequently,a GPU,instead of a CPU,was used to calculate the acceleration ratio for handling airborne gamma-ray spectral data of different volumes,and wavelet basis functions were used for those with the same data volume.Finally,by introducing white noise to the experimentally measured airborne gamma-ray spectral data,the signal-to-noise ratio of denoised data was calculated to optimize the threshold denoising method suitable for parallel acceleration of the GPU.[Results]The optimal two-dimensional block sizes for denoising airborne gamma-ray spectral data are 64×64 and 128×128.Among the wavelet basis functions,those that achieved a total time acceleration ratio exceeding 100 compared to CPU processing account for 80%,while those that reached an acceleration ratio exceeding 90 constitute 91%.The coif5 function achieves an acceleration ratio of 353 times whilst the acceleration ratio of the threshold denoising function approaches 570.[Conclusions]All wavelet functions exhibit insufficient denoising effects at low signal-to-noise ratios and excessive denoising effects at high signal-to-noise ratios.Significant denoising can be achieved using hard thresholding of coif5,soft thresholding of coif1,and improved thresholding of bior3.7. |
| Author | 吴和喜 王鑫杰 熊超 王欣 |
| AuthorAffiliation | 东华理工大学 核科学与工程学院 南昌 330013%苏州大学 医学部放射医学与防护学院 苏州 215123 |
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| Author_FL | XIONG Chao WANG Xin WANG Xinjie WU Hexi |
| Author_FL_xml | – sequence: 1 fullname: XIONG Chao – sequence: 2 fullname: WANG Xin – sequence: 3 fullname: WANG Xinjie – sequence: 4 fullname: WU Hexi |
| Author_xml | – sequence: 1 fullname: 熊超 – sequence: 2 fullname: 王欣 – sequence: 3 fullname: 王鑫杰 – sequence: 4 fullname: 吴和喜 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | CUDA-based parallel acceleration algorithm for wavelet denoising of airborne γ-ray spectrometry data |
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| Keywords | Airborne gamma-ray spectra 阈值降噪 图形处理器 Threshold denoising 航空γ能谱 GPU |
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| Publisher | 东华理工大学 核科学与工程学院 南昌 330013%苏州大学 医学部放射医学与防护学院 苏州 215123 |
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| Title | 基于CUDA的航空γ能谱数据小波降噪并行加速算法 |
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