This thesis by Robert Strzodka describes the design of robust quantized schemes and their hardware efficient implementation on data-stream-based architectures for PDE-based image processing. The focus lies on enhancing both performance and accuracy by an efficient use of appropriate hardware resources. Quantized schemes which, despite roundoff errors, preserve the qualitative behavior of the continuous models are constructed, and examined on different GPUs, a FPGA and a reconfigurable array processor. The pros and cons of the hardware designs and the memory gap problem are discussed in detail. (Hardware Efficient PDE Solvers in Quantized Image Processing. Robert Strzodka. PhD thesis, University of Duisburg-Essen, 2004.)

We received news simultaneously from the developers of two new GPU ray tracers. Both projects are graduate-level thesis projects. One, called GPU-RT, is developed by Martin Christen and supports .3DS format meshes, multiple materials, and implements acceleration data structures. GPU-RT runs on NVIDIA GeForce 6 Series GPUs under D3D/HLSL and OpenGL/GLSL, and is available on SourceForge.net. The other project, “Ray Tracing on Programmable Graphics Hardware”, is by Filip Karlsson and Carl Johan Ljungstedt of Chalmers University of Technology. The thesis describes, among other things, how proximity clouds can be used to accelerate ray tracing on the GPU. (1. GPU-RT, Diploma Thesis by Martin Christen. 2. “Ray Tracing on Programmable Graphics Hardware”, Masters Thesis by Filip Karlsson and Carl Johan Ljungstedt.)

In this masters thesis by Marcin Jdrzejewski, ray tracing is implemented on the GPU to accelerate computation of sound paths between sound sources and receivers. Each ray averages 16-20 wall reflections, and those rays that intersect a sphere approximating the receiver are included in an echogram that is used in the auralization process. Typically 4096 rays are used, but the application can run in real time with up to 64K rays. A demo application, article and some movies can be downloaded from the following link. (Computation of room acoustics on a GPU.)

This dissertation by Tim Purcell of Stanford University discusses several topics relevant to GPGPU including a stream processor abstraction for GPUs, and GPU-based ray tracing and photon mapping algorithms. Much of this work has been reported on GPGPU before, but the description of the ray tracing work in particular is expanded and updated from previous papers with details about the Radeon 9700 ray tracer demonstrated at Siggraph 2002. Included on the web page are links to the dissertation defense talk slides and movies of the various demos. (Ray Tracing on a Stream Processor, Timothy J. Purcell, Ph.D. Dissertation, March 2004.)

This dissertation by Mangesh Nijasure of University of Central Florida presents a system for computing plausible global illumination solutions for dynamic environments in real time on programmable graphics processors (GPUs). The dissertation describes a progressive global illumination algorithm to simulate multiple bounces of light on the surfaces of synthetic scenes. The entire algorithm runs on an ATI Radeon 9800 using vertex and fragment shaders, and computes global illumination solutions for reasonably complex scenes with moving objects and moving lights in real time. (Real-time Global Illumination on GPU, Mangesh Nijasure. MS Thesis, Fall 2003)

This dissertation by Mark Harris of UNC Chapel Hill contains significant GPGPU-related content, including physically-based simulation of fluids, clouds, and chemical reaction diffusion on GPUs. The material is much expanded from several papers summarizing various components of the work, most of which have been reported on this site previously. Also included in the dissertation is a history of general-purpose computation on graphics hardware. (Real-Time Cloud Simulation and Rendering. Mark J. Harris. Ph.D. Dissertation. UNC Technical Report #TR03-040. September, 2003.)