A MULTI-LEVEL IMPLEMENTATION OF IMAGE AMPLIFICATION ON THE GENERAL PURPOSE GRAPHICAL PROCESSING UNIT

Image amplification is an important image enhancement technique for applications such as medicine, satellite imaging, forensic sciences, remote sensing, among others. The existing techniques are highly computationally intensive and take a lot of time to execute on conventional processors. Their highly computationally intensive nature makes them a good fit for massively parallel architectures such as the general-purpose graphical processing unit (GPGPU) devices. In this research, we accelerate a state-of-the-art image amplification technique on Nvidia’s GPGPU device, Kepler GK110 using the Compute Unified Device Architecture (CUDA) programming model. The technique comprises four computationally intensive stages namely Canny edge detection, vertical edge preservation, horizontal edge preservation and mean preserving interpolation. Using efficacious CUDA optimization techniques, we successively map the four stages of the algorithm to the GPGPU device, creating a hierarchy of five implementations. The final implementation of the hierarchy completely maps all of the algorithm stages to the GPGPU device, eliminating any costly intermediate host-device computations and focusing more on useful computations. We provide a detailed analysis of the kernel time and end-to-end application time obtained for each implementation in the hierarchy. We also compare the GPGPU execution time for each algorithm stage with the equivalent serial implementation. We discuss an empirical method for identifying optimal GPGPU execution configuration to maximize the device utilization. All of the GPGPU kernels executed on the Kepler GPGPU device achieve high speedup, as high as 90x, versus the optimized serial implementation. In addition, for the largest image size of 10240x10240, the most optimal GPGPU implementation achieves an end-to-end application speedup of 11.75x versus the serial counterpart. The research also presents the analysis on the implementation of the application on Amazon Web Services’ instances. This analysis further provides an opportunity to study the scalability of the application