GPUCV is a free GPU-accelerated library for image processing and computer vision. It offers an Intel OPENCV-like programming interface for easily porting existing applications. A one-page description is available. A longer presentation and discussion was published at IEEE ICME 2006. (J.-P. Farrugia, P. Horain, E. Guehenneux, Y. Allusse, “GPUCV: A framework for image processing acceleration with graphics processors”, CDROM proc. of the IEEE International Conference on Multimedia & Expo, July 9-12, 2006, Toronto, Ontario, Canada.)

This survey paper by D. Göddeke and R. Strzodka compares native double precision solvers for linear systems of equations as they typically arise in finite element discretizations with emulated- and mixed-precision schemes. Such schemes are particularly suitable for coupled hardware configurations such as GPUs and FPGAs, which serve as co-processors to the general purpose CPU. The results demonstrate that

1. accuracy is preserved even for very ill-conditioned systems,
2. significant speedups can be achieved (time aspect, GPUs) and
3. area requirements are reduced (space aspect, FPGA).

(link/preprint)

With their upcoming publication in Computer Graphics Forum, Owens et al. have revised their 2005 comprehensive survey of the history and state of the art in GPGPU. It describes, summarizes and analyzes the latest research in mapping general-purpose computation to graphics hardware. The report begins with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. The authors describe the techniques used in mapping general-purpose computation to graphics hardware, and survey and categorize the latest developments in general-purpose application development on graphics hardware. (A Survey of General-Purpose Computation on Graphics Hardware. John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, Timothy J. Purcell, in “Computer Graphics Forum”, Volume 26, number 1, pp 80-113. 2007. To appear.)

This Ph.D. dissertation by Aaron Lefohn at the University of California, Davis describes the Glift GPU data structure abstraction and its application to both GPU-based data-parallel and interactive rendering algorithms. The applications include octree 3D painting, adaptive shadow maps, resolution matched shadow maps, heat-diffusion depth-of-field, and a GPU-based direct solver for tridiagonal linear systems. While much of this work has been posted previously, this dissertation contains a more in-depth discussion of the Glift data structure library and introduces several GPGPU and rendering algorithms that are not yet published. This dissertation demonstrates that a data structure abstraction for GPUs can simplify the description of new and existing data structures, stimulate development of complex GPU algorithms, and perform equivalently to hand-coded implementations. The dissertation also presents a case that future interactive rendering solutions will be an inseparable mix of general-purpose, data-parallel algorithms and traditional graphics programming. (Aaron Lefohn, “Glift: Generic Data Structures for Graphics Hardware”, Ph.D. dissertation, Computer Science Department, University of California Davis, September 2006.)

This Pixar Animation Studios Technical Report by Kass, Lefohn, and Owens describes a GPU-based data-parallel direct tridiagonal linear solver. To the authors’ knowledge, this is the first reported direct, linear-time tridiagonal GPU solver. The solver is used to implement a new heat-diffusion-based depth-of-field preview algorithm; and the paper describes solving thousands of tridiagonal systems, each with hundreds of elements, on the GPU at interactive rendering rates. The alternating direction implicit solution gives rise to separable spatially varying recursive (infinite-impulse response, IIR) filters that can compute large-kernel convolutions in constant time per pixel while respecting the boundaries between in-focus and out-of-focus objects. Recursive filters have traditionally been viewed as problematic for GPUs, but using the well-established method of cyclic reduction of tridiagonal systems, the authors are able to parallelize the computation and implement an efficient solution in terms of GPGPU primitives. (Michael Kass, Aaron Lefohn, and John Owens. Interactive Depth of Field Using Simulated Diffusion on the GPU, Technical Report #06-01, Pixar Animation Studios, January 2006.)

With their upcoming publication in Computer Graphics Forum, Owens et al. have revised their 2005 comprehensive survey of the history and state of the art in GPGPU. It describes, summarizes and analyzes the latest research in mapping general-purpose computation to graphics hardware. The report begins with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. The authors describe the techniques used in mapping general-purpose computation to graphics hardware, and survey and categorize the latest developments in general-purpose application development on graphics hardware. (A Survey of General-Purpose Computation on Graphics Hardware. John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, Timothy J. Purcell, in “Computer Graphics Forum”, Volume 26, 2007. To appear.)

This tutorial explains how global illumination rendering methods can be implemented on Shader Model 3.0 GPUs. These algorithms do not follow the conventional local illumination model of DirectX/OpenGL pipelines, but require global geometric or illumination information when shading a point. In addition to the theory and state of the art of these approaches, the tutorial goes into the details of a few algorithms, including mirror reflection, refraction, caustics, diffuse/glossy indirect illumination, precomputation-aided global illumination for surface and volumetric models, obscurances and tone mapping, also giving their GPU implementation in HLSL or Cg language. (Laszlo Szirmay-Kalos, Laszlo Scecsi, Mateu Sbert: GPUGI: Global Illumination Effects on the GPU. Eurographics 2006 Tutorial.)

A multi-view stereo evaluation has been proposed by Steve Seitz et al. The challenge involves recovering 3D reconstructions of complete objects from a large number of views. Among the reported techniques, two out of nine make an intensive usage of GPUs, both yielding large speedups: the work by Pons, Keriven and Labatut that took part in the original competition at CVPR06, and the work by Hornung and Kobbelt. Running times, accuracy and completeness of the methods are reported here. (Steve Seitz et al. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, 2006.)

This white paper from RapidMind and HP compares the performance of BLAS dense linear algebra operations, the FFT, and European option pricing on the GPU against highly tuned CPU implementations on the fastest available CPUs. All of the GPU implementations were made using the RapidMind Development Platform, which allows the use of standard C++ programming to create high-performance parallel applications that run on the GPU. The full source for the samples is available in conjunction with a new beta version of the RapidMind development platform. The results will also be presented as a poster at SC06.
(http://rapidmind.net/sc06_hp_rapidmind_cpugpu_summary.php)

The computational power of GPU-based algorithms is receiving increased attention in research on Computer Vision and 3D stereo reconstruction from images. In this context one of the most important ingredients for any 3D stereo reconstruction technique is the estimation of photo consistency. This ECCV06 paper presents a new, illumination invariant photo consistency measure for high quality, volumetric 3D reconstruction from calibrated images. In contrast to current standard methods such as normalized cross-correlation it supports unconstrained camera setups and non-planar surface approximations. The paper shows how this measure as well as the other important stages of the volumetric reconstruction pipeline can be implemented in a highly efficient way by exploiting current graphics processors. The authors’ GPU implementation achieves speedups up to a factor of 85 in comparison to CPU-based algorithms, and allows reconstruction of high quality models with computation times of only a few seconds to minutes, even for large numbers of cameras and high volumetric resolutions. (Robust and Efficient Photo-Consistency Estimation for Volumetric 3D Reconstruction. Alexander Hornung and Leif Kobbelt. European Conference on Computer Vision (ECCV 2006), LNCS, vol. 3952, Springer, 179-190.)