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Comparison of muItihardware parallel implementations for a phase unwrapping algorithm

机译:相位展开算法的多硬件并行实现的比较

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摘要

Phase unwrapping is an important problem in the areas of optical metrology, synthetic aperture radar (SAR) image analysis, and magnetic resonance imaging (MRI) analysis. These images are becoming larger in size and, particularly, the availability and need for processing of SAR and MRI data have increased significantly with the acquisition of remote sensing data and the popularization of magnetic resonators in clinical diagnosis. Therefore, it is important to develop faster and accurate phase unwrapping algorithms. We propose a parallel multigrid algorithm of a phase unwrapping method named accumulation of residual maps, which builds on a serial algorithm that consists of the minimization of a cost function; minimization achieved by means of a serial Gauss-Seidel kind algorithm. Our algorithm also optimizes the original cost function, but unlike the original work, our algorithm is a parallel Jacobi class with alternated minimizations. This strategy is known as the chessboard type, where red pixels can be updated in parallel at same iteration since they are independent. Similarly, black pixels can be updated in parallel in an alternating iteration. We present parallel implementations of our algorithm for different parallel multicore architecture such as CPU-multicore, Xeon Phi coprocessor, and Nvidia graphics processing unit. In all the cases, we obtain a superior performance of our parallel algorithm when compared with the original serial version. In addition, we present a detailed comparative performance of the developed parallel versions.
机译:相位展开是光学计量,合成孔径雷达(SAR)图像分析和磁共振成像(MRI)分析领域中的重要问题。这些图像的尺寸越来越大,尤其是随着遥感数据的获取和磁谐振器在临床诊断中的普及,SAR和MRI数据的可用性和处理需求显着增加。因此,开发更快,更准确的相位展开算法很重要。我们提出了一种相位展开方法的并行多重网格算法,称为残差图累积,它基于一种串行算法,该算法包括最小化代价函数。通过串行高斯-塞德尔算法实现最小化。我们的算法还优化了原始成本函数,但是与原始工作不同,我们的算法是具有交替最小化的并行Jacobi类。这种策略称为棋盘类型,其中红色像素是独立的,因此可以在同一迭代中并行更新红色像素。类似地,可以在交替迭代中并行更新黑色像素。我们为不同的并行多核体系结构(例如CPU多核,至强融核协处理器和Nvidia图形处理单元)提供了算法的并行实现。在所有情况下,与原始串行版本相比,我们的并行算法均具有出色的性能。此外,我们还介绍了开发的并行版本的详细比较性能。

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