Heterogeneous parallel computing accelerated iterative subpixel digital image correlation

Parallel computing techniques have been introduced into digital image correlation （DIC） in recent years and leads to a surge in computation speed. The graphics processing unit （GPU）-based parallel computing demonstrated a surprising effect on accel- erating the iterative subpixel DIC, compared with CPU-based parallel computing. In this paper, the performances of the two kinds of parallel computing techniques are compared for the previously proposed path-independent DIC method, in which the initial guess for the inverse compositional Gauss-Newton （IC-GN） algorithm at each point of interest （POI） is estimated through the fast Fourier transform-based cross-correlation （FFT-CC） algorithm. Based on the performance evaluation, a heterogeneous parallel computing （HPC） model is proposed with hybrid mode of parallelisms in order to combine the computing power of GPU and multicore CPU. A scheme of trial computation test is developed to optimize the configuration of the HPC model on a specific computer. The proposed HPC model shows excellent performance on a middle-end desktop computer for real-time subpixel DIC with high resolution of more than 10000 POIs per frame.