Complexity Analysis and Parallel Implementation of the Well Optimized Linear Finder (WOLF) Atmospheric Turbulence Compensation (ATC) Algorithm on GPU Technology with Comparative Analysis of Traditional Phase Diverse ATC Methods

Abstract

A new high-speed, high-accuracy general transfer function estimation method for diversitybased problems, known as the Well Optimized Linear Finder Algorithm (WOLF Algorithm), has been developed [1]. Compared to the traditional phase diversity ATC methods, the WOLF Algorithm has less computational complexity and better approximation of the Optical Transfer Function (OTF) and consequently the entrance pupil phases. We compare the time complexity between the traditional Phase Diversity atmospheric turbulence compensation approach and the WOLF Algorithm. The results show that the WOLF Algorithm is dramatically faster than traditional phase diversity methods theoretically. We also implement the time complexity results of the WOLF methodology using serial processing on a Central Processing Unit (CPU) architecture (2.8 GHz Quad-Core Intel Core i7 processor). The results show that the simulated run time for the WOLF methodology with a 256 × 256 pixel image is 8.2 seconds which matches our theoretical results within one standard deviation. Finally, we simulate the WOLF methodology time complexity using parallel processing on several GPUs. The results for the WOLF methodology with a 256 × 256 pixel image on the fastest GPU processor (Nvidia Tesla T100) was 3.7740 × 10−3 seconds (264.97 Hz), which is 1224.05 times faster than the implementations on a CPU architecture.