Implementation of edge-enhancement nonlinear anisotropic diffusion filtering using different CUDA memory models

Graphics Processing Units (GPUs) are used today as affordable energy-efficient method of acceleration for computationally exhaustive algorithms to decrease execution time exploiting the power of parallel programing techniques. In the field of medical imaging, GPUs became crucial acceleration method for computationally exhaustive algorithms. This paper presented the effect of memory optimization on the performance of CUDA accelerated medical image processing algorithms. Edge-enhancement nonlinear anisotropic diffusion is used as a case study to show the effect of utilization of different memory types on the performance.