This paper by Banterle and Giacobazzi at UniversitÃ degli Studi di Verona presents an efficient implementation of the Octagon Abstract Domain (OAD) on graphics hardware. OAD is a relational numerical abstract domain which approximates invariants as conjunctions of constraints of the form +/- x +/- y <= c, where x and y are program variables and c is a constant which can be an integer, rational or real. OAD has been used with success in the aerospace industry for analyzing C programs such as the flight control software for the Airbus A340 fly-by-wire system. ( A Fast Implementation of the Octagon Abstract Domain on Graphics Hardware. Francesco Banterle and Roberto Giacobazzi. Proceeding of The 14th International Static Analysis Symposium (SAS). 2007)
This paper outlines how GPGPU techniques can be used for Monte Carlo simulations of quantum field theories such as QCD. The speedup is around a factor of 4-10 depending on the GPU model relative to SSE optimized code on a Pentium 4. Sample code is also given. (Lattice QCD as a video game)
According to an article on Extremetech.com , French company GPU-Tech has announced Ecolib, a series of C++ libraries for GPGPU which target both ATI and NVIDIA GPUs. A PDF describing the API is available. Their download page includes demo software with code samples and workstation CPU/GPU benchmarking tools.
This technical report by N. Cuntz, R. Strzodka and A. Kolb describes a particle level set (PLS) system for fast and accurate surface tracking on the GPU. The technique demonstrates the coupling of grid and particle information by using vertex/fragment buffer objects, shaders and blending functionality in an innovative way. Improvements over the original PLS technique include a sub-voxel interface representation and a more accurate level set correction using more precise particle radii. As a concrete application the authors demonstrate that their fast and accurate PLS is well suited to the visualization of dynamic flows. An accurate evolution of time surfaces and representation of path volumes offer a more reliable basis for data interpretation. (Real-Time Particle Level Sets with Application to Flow Visualization. Technical report, 2007)
From the Evolved Machines Website:
“We simulate neuronal components closely modeled after neurons in the brain, and synthesize arrays which wire themselves by simulating neural circuit growth in three dimensions. We are the first company to harness the power of programmable GPUs for the simulation of neural computation, now achieving 100-fold acceleration of the computing power of conventional cores. We are also designing the first generation of devices truly based on brain circuitry, pioneering the fusion of neuroscience and engineering to develop new categories of machines which embed some of the capacities of biological neural systems.”
This paper by Robert et al. at the University of Bern, Switzerland describes the object intersection buffer (OIB), a GPU-based visibility preprocessing algorithm for accelerating ray tracing. Based on this approach, a hybrid ray tracer is proposed to exploit parallel ray tracing using the GPU and CPU. (Hybrid Ray Tracing – Ray Tracing Using GPU-Accelerated Image-Space Methods. Philippe C.D. Robert, Severin Schoepke, and Hanspeter Bieri. Proceedings of GRAPP 2007.)
Radio wave propagation predictions are of great interest for cellular radio networks. Ray tracing approaches are an established technique for wave propagation, however, such approaches need to be extended to include diffraction, which is a predominant effect for common mobile radio frequencies. We demonstrate how to exploit the GPU to accelerate wave propagation predictions by orders of magnitude, making them available at interactive frame rates. The paper presents a GPU implementation of our diffraction technique. The presented technique can be easily extended to also simulate the diffraction of water waves by obstacles in complex three dimensional scenarios in a physically correct manner. (Fast Edge-Diffraction-Based Radio Wave Propagation Model for Graphics Hardware. Tobias Rick, Rudolf Mathar, Proceedings of ITG INICA 2007)
This work approaches the fundamental problem of accelerating FFT computation by use of GPUs, in order to apply it to Adaptive Optics, the key for obtaining the maximum performance from projected ground-based eXtremely Large Telescopes. A method to efficiently adapt the FFT for the underlying architecture of GPUs is given. The authors derive a novel FFT method that alternates base-2 and base-4 decomposition of the bidimensional domain to take the most from Multiple Render Target extension as they elaborate a very unusual Pease 8-data “butterfly”. (Modal Fourier wavefront reconstruction using GPUs J.G. Marichal-Hernandez, J.M. Rodriguez-Ramos, F. Rosa. La Laguna University. To appear in Journal of Electronic Imaging.)
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.)
Neoptica has recently posted a whitepaper, “Programmable Graphicsâ€”The Future of Interactive Rendering.” It introduces the coming era of programmable graphics, in which developers implement rendering algorithms using combinations of parallel CPU and GPU tasks executing cooperatively on heterogeneous multi-core architectures of the near future. By embracing both task- and data-parallel computation, this approach frees developers to use the most efficient parallel computation style for their algorithms, and makes it possible to define custom graphics pipelines built using complex algorithms and dynamic data structures. The paper argues that future graphics applications that leverage the tightly coupled capabilities of forthcoming CPUs and GPUs will generate far richer and more realistic imagery, use computational resources more efficiently, and scale to large numbers of CPU and GPU cores.