基于CUDA的加速劳厄深度重建算法
Accelerating Laue Depth Reconstruction Algorithm with CUDA
摘要：
劳厄衍射显微镜实验使用多色劳厄微衍射技术来检查材料的结构，在所有三个维度上具有亚微米空间分辨率。在该实验期间，测量局部晶体学取向、取向梯度和应变作为将以HDF5图像格式记录的特性。记录的图像将用深度重建算法处理，用于未来的数据分析。但是目前的深度重建算法消耗了相当多的处理时间，并且可能需要长达2周的时间来重建从单个实验收集的数据。为了提高深度重建的计算速度，本文提出了一种可扩展的GPU程序解决方案的深度重建问题。测试结果表明，对于各种大小的输入数据，运行时间将比现有的CPU设计快10到20倍。
英文摘要：
The Laue diffraction microscopy experiment uses the polychromatic Laue micro-diffraction technique to examine the structure of materials with sub-micron spatial resolution in all three dimensions. During this experiment, local crystallographic orientations, orientation gradients and strains are measured as properties which will be recorded in HDF5 image format. The recorded images will be processed with a depth reconstruction algorithm for future data analysis. But the current depth reconstruction algorithm consumes considerable processing time and might take up to 2 weeks for reconstructing data collected from one single experiment. To improve the depth reconstruction computation speed, we propose a scalable GPU program solution on the depth reconstruction problem in this paper. The test result shows that the running time would be 10 to 20 times faster than the prior CPU design for various size of input data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
