High accuracy digital image correlation powered by GPU-based parallel computing

• This paper reports the implementation of the path-independent DIC on GPU device.
• The IC-GN algorithm-based DIC is accelerated for nearly two orders of magnitude.
• Parallel computing-powered sub-pixel DIC achieves the highest speed ever reported.

A sub-pixel digital image correlation (DIC) method with a path-independent displacement tracking strategy has been implemented on NVIDIA compute unified device architecture (CUDA) for graphics processing unit (GPU) devices. Powered by parallel computing technology, this parallel DIC (paDIC) method, combining an inverse compositional Gauss–Newton (IC-GN) algorithm for sub-pixel registration with a fast Fourier transform-based cross correlation (FFT-CC) algorithm for integer-pixel initial guess estimation, achieves a superior computation efficiency over the DIC method purely running on CPU. In the experiments using simulated and real speckle images, the paDIC reaches a computation speed of 1.66×105 POI/s (points of interest per second) and 1.13×105 POI/s respectively, 57–76 times faster than its sequential counterpart, without the sacrifice of accuracy and precision. To the best of our knowledge, it is the fastest computation speed of a sub-pixel DIC method reported heretofore.