AIR: Accelerated Image Registration

This thesis presents a performance analysis of an accelerated 2-D rigid image registration implementation that employs the Compute Unified Device Architecture (CUDA) programming environment to take advantage of the parallelprocessing capabilities of NVIDIA's Tesla C870 GPU. We explain the underlying structure of the GPU implementation and compare its performance and accuracy against a fast CPU-based implementation. Our experimental results demonstrate that our GPU version is capable of up to 90ÌÄ' speedup with bilinear interpolation and 30ÌÄ' speedup with bicubic interpolation while maintaining a high level of accuracy. This compares favorably to recent image registration studies, but it also indicates that our implementation only reaches about 70% of theoretical peak performance. To analyze our results, we utilize profiling data to identify some of the underlying limitations of CUDA that prohibit peak performance. At the end, we emphasize the need to manage memory resources carefully to fully utilize theGPU and obtain maximum speedup.