This tutorial is about GPU computing on NVIDIA hardware with a special focus on image processing and computer vision applications. After an introduction to the CUDA architecture and the latest NVIDIA GPU hardware capabilities two APIs for GPGPU computing, CUDA C and OpenCL, are discussed. Best implementation practices for achieving optimal performance are highlighted by examples. Finally we will cover available tools for debugging and profiling as well as GPU accelerated libraries for computer vision and image processing tasks.
Timo Stich is a Developer Technology Engineer for NVIDIA Corporation. His focus is on image processing applications of Graphics Processors. Prior to joining NVIDIA he was research staff in Computer Graphics and Image Processing at the Max-Planck-Institute for Computer Science and the Computer Graphics Lab of Brunswick University, Germany. He received a diploma degree in Computer Science from Mannheim University, Germany and a Ph.D. degree from the Brunswick University, Germany.