Abstract: Big improvements in the performance of graphics processing units (GPUs) turned them into a compelling platform for high performance computing. In this thesis, we discuss the usage of NVIDIA's CUDA in two applications -- Einstein@Home, a distributed computing software, and OpenSteer, a game-like application. Our work on Einstein@Home demonstrates that CUDA can be integrated into existing applications with minimal changes, even in programs designed without considering GPU usage. However the existing data structure of Einstein@Home performs poorly when used on the GPU. We demonstrate that using a redesigned data structure improves the performance to about three times as fast as the original CPU version, even though the code executed on the device is not optimized. We further discuss the design of a novel spatial data structure called "dynamic grid" that is optimized for CUDA usage. We measure its performance by integrating it into the Boids scenario of OpenSteer. Our new concept outperforms a uniform grid by a margin of up to 15%, even though the dynamic grid still provides optimization potential. (Case studies on gpu usage and data structure design. J. Breitbart, Master's thesis, Universität Kassel, 2008)

This paper described an implementation of fast deformable image registration using GPUs and CUDA in radiation therapy. Using lung and prostate volumetric imaging, the GPU implementation is 40-66 times faster than a single-threaded CPU implementation and 25-41 times faster than a multithreaded implementation. The paradigm of GPU-based near-real-time deformable image registration opens up a host of clinical applications for medical imaging. ( High performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy. (Sanjiv S. Samant, Junyi Xia, Pinar Muyan-Özçelik, John D. Owens. Medical physics, 2008.)