{"title":"用于光学光谱图像分析的 GPU 加倍算法","authors":"M. Milanič, Rok Hren","doi":"10.3390/a17020074","DOIUrl":null,"url":null,"abstract":"The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg–Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a 220 × 100 × 61 image (x-axis × y-axis × spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"2 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images\",\"authors\":\"M. Milanič, Rok Hren\",\"doi\":\"10.3390/a17020074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg–Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a 220 × 100 × 61 image (x-axis × y-axis × spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images.\",\"PeriodicalId\":502609,\"journal\":{\"name\":\"Algorithms\",\"volume\":\"2 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Algorithms\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/a17020074\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Algorithms","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/a17020074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
加倍(AD)算法是辐射传递方程(RTE)的通用解析解。AD 在精度和计算效率之间取得了良好的平衡,在速度方面超过了蒙特卡罗(MC)模拟等其他 RTE 解法,而在精度方面则优于扩散逼近法等近似解法。虽然 AD 算法传统上是在中央处理器(CPU)上实现的,但本研究侧重于利用图形处理器(GPU)的功能来提高计算速度。在处理速度方面,GPU AD 算法比 GPU MC 方法提高了约 5000 到 40000 倍。该算法的最佳线程数约为 3000。为了说明 GPU AD 算法的实用性,我们使用 Levenberg-Marquardt 逆解法从各种血液动力学条件下的人体皮肤光学光谱数据中提取对象参数。在计算效率方面,处理一幅 220 × 100 × 61(x 轴 × y 轴 × 光谱轴)的图像大约需要 5 分钟。与其他 RTE 解决方案相比,GPU AD 算法的开发在确定组织属性方面取得了进步。此外,GPU AD 方法本身也具有在光谱图像分析中加速机器学习技术的潜力。
GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg–Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a 220 × 100 × 61 image (x-axis × y-axis × spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images.