Efficient GPU Implementation of Lucas-Kanade through OpenACC

Optical flow estimation stands as an essential component for motion detection and object tracking procedures. It is an image processing algorithm, which is typically composed of a series of convolution masks (approximation of the derivatives) followed by 2 x 2 linear systems for the optical flow vectors. Since we are dealing with a stencil computation for each stage of the algorithm, the overhead from memory accesses is expected to be significant and to yield a genuine scalability bottleneck, especially with the complexity of GPU memory configuration. In this paper, we investigate a GPU deployment of an optimized CPU implementation via OpenACC, a directive-based parallel programming model and framework that ease the process of porting codes to a wide-variety of heterogeneous HPC hardware platforms and architectures. We explore each of the major technical features and strive to get the best performance impact. Experimental results on a Quadro P5000 are provided together with the corresponding technical discussions, taking the performance of the multicore version on a INTEL Broadwell EP as the baseline.