GPU Accelerated Digital Volume Correlation

Abstract

A sub-voxel digital volume correlation (DVC) method combining the 3D inverse compositional Gauss-Newton (ICGN) algorithm with the 3D fast Fourier transform-based cross correlation (FFT-CC) algorithm is proposed to eliminate path-dependence in current iterative DVC methods caused by the initial guess transfer scheme. The proposed path-independent DVC method is implemented on NVIDIA compute unified device architecture (CUDA) for GPU devices. Powered by parallel computing technology, the proposed DVC method achieves a significant improvement in computation speed on a common desktop computer equipped with a low-end graphics card containing 1536 CUDA cores, i.e., up to 23.3 times faster than the sequential implementation and 3.7 times faster than the multithreaded implementation of the same DVC method running on a 6-core CPU. This speedup, which has no compromise with resolution, accuracy and precision, benefits from the coarse-grained parallelism that the points of interest (POIs) are processed simultaneously and also from the fine-grained parallelism that the calculation at each POI is performed with multiple threads in GPU. The experimental study demonstrates the superiority of the GPU-based parallel computing for acceleration of DVC over the multi-core CPU-based one, in particular on a PC level computer.