Two‐dimensional digital image correlation (2D‐DIC) is an experimental technique used to measure in‐plane displacement of a test specimen. Real‐time measurement of full‐field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU‐based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU‐GPU) framework in combination with a pyramid‐based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by compared to a sequential CPU‐based implementation for a pair of gray‐scale images with a resolution of pixels. As an important milestone, feasibility to measure deformations in real time ( 1 s) is manifested in this study.

中文翻译：

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使用异构（CPU-GPU）框架的高速二维数字图像相关算法

二维数字图像关联（2D-DIC）是一种用于测量试样平面内位移的实验技术。由于算法的巨大计算量，因此实时测量全场位移数据具有挑战性。为了提高计算速度，最近的研究工作集中在使用图形处理单元（GPU）跨图像对内的子集进行并行化的方法。但是，尚未探索根据数据处理顺序来提高该算法性能的其他基于GPU的并行化方法。为了弥补这一研究空白，我们的方法利用了子集内以及跨子集内的并行性迭代周期中每个计算步骤的子集。本文提出了一种异构（CPU-GPU）框架，并结合了基于金字塔的子集初始值估计（并行）。所提出框架的预计算步骤是使用CPU实现的，而主要的迭代步骤是使用GPU实现的。事实证明，与一对像素分辨率的灰度图像的基于顺序CPU的实现相比，所提出的异构框架的总体计算速度得到了提高。作为一个重要的里程碑，这项研究显示了实时测量变形（ 1 s）的可行性。