Hardware Assisted Natural Neighbour Interpolation

December 12th, 2004

Natural neighbour interpolation is a popular nonparametric method for interpolating among sample data points and is based on computing Voronoi diagrams. In this paper by Fan, Efrat, Koltun, Krishnan and Venkatasubramanian, we show how natural neighbour interpolation can be performed very efficiently on the GPU. The main advantage of this approach is that multiple interpolation queries can be issued simultaneously; the algorithm creates a scalar field for the interpolated result. (Hardware Assisted Natural Neighbour Interpolation. Quanfu Fan, Alon Efrat, Vladlen Koltun, Shankar Krishnan, and Suresh Venkatasubramanian. Proc. 7th Workshop on Algorithms Engineering and Experimentation.)

Real-Time Motion Estimation and Visualization on Graphics Cards

November 27th, 2004

This paper by Strzodka and Garbe presents a tool for real-time visualization of motion features in 2D image sequences. The motion is estimated through an eigenvector analysis of the spatio-temporal structure tensor at every pixel location. Post-processing in the form of coloring, blending, threshholding, fading and smoothing helps to select the desired motion features for display. The paper demonstrates several examples of test sequences containing people moving at different velocities. These people are visually marked in the real-time display of the image sequence. The tool is also applied to angiography sequences to emphasize the blood flow and its distribution. The implementation uses DX9 graphics hardware and centers around a vectorized version of the Jacobi method for matrix diagonalization. (Real-Time Motion Estimation and Visualization on Graphics Cards. Robert Strzodka and Christoph Garbe in Proceedings of Visualization 2004, pages 545-552, 2004)

Real-Time 3D Fluid Simulation on the GPU with Complex Obstacles

November 27th, 2004

This Pacific Graphics 2004 paper by Youquan Liu et al. presents a way to process complex boundary conditions when simulating fluid flow using the Navier-Stokes Equations on the GPU. After voxelizing the 3D geometry scene, this technique computes a “modification factor texture” and an offset texture in “flat 3D” form to delineate boundary conditions needed to handle the internal obstacles, and in this way it takes advantage of the parallelism of GPU to accelerate the whole computation. (“Real-Time 3D Fluid Simulation on GPU with Complex Obstacles”, Youquan Liu, Xuehui Liu and Enhua Wu, In Proceedings of Pacific Graphics 2004, pages 247-256,October 2004.)

GPGPU Course at UNC Chapel Hill

October 29th, 2004

A course on general-purpose computation on graphics processors is being taught this semester by Dinesh Manocha at The University of North Carolina at Chapel Hill. This seminar course covers algorithmic and system issues as well as a number of applications. It will review the current state of the art and investigate many open issues. (GPGP)

Course on GPGPU at University of Aarhus, Denmark

October 29th, 2004

A short course on GPGPU is given at the Department of Computer Science, University of Århus, Denmark by Jesper Mosegaard and Thomas Sangild Sørensen. (GPGPU_E04)

Scout: A Hardware-Accelerated System for Quantitatively Driven Visualization and Analysis

October 20th, 2004

This IEEE Visualization 2004 paper by McCormick et al. describes the Scout System and Language that allow the GPU to be programmed for scientific visualization. Scout uses a data parallel language that allows the user to program visual mappings from data values to the final rendered result. These techniques can be used to replace standard user interface components, such as the transfer function editor commonly used in volume rendering. (“Scout: A Hardware-Accelerated System for Quantitatively Driven Visualization and Analysis”, Patrick S. McCormick, Jeff Inman, James P. Ahrens, Chuck Hansen and Greg Roth, In Proceedings IEEE Visualization 2004, pages 171-178, October 2004.)

GPGPU Course Notes from IEEE Visualization 2004

October 20th, 2004

The complete course notes have been posted for the full-day GPGPU course held at IEEE Visualization 2004. The course, titled “GPGPU: General-Purpose Computing on Graphics Processors“, was held on Monday, October 11th, 2004 in Austin, Texas. The course begins with the architectural, economic, and programmatic motivations behind GPGPU. It then introduces a “hello world” GPGPU example and describes the stream programming model in detail (including Brook). Mathematical and algorithmic primitives are then presented, followed by descriptions of many of the low-level technical details required for effective real-world GPGPU programming. The course concludes with several case studies and a disscusison of the future architectual, application, and research possibilities for GPGPU. The course organizer was Aaron Lefohn, and the presenters were Ian Buck, Aaron Lefohn, John Owens, and Robert Strzodka. ( “GPGPU: General-Purpose Computing on Graphics Processors,” IEEE Visualization 2004)

GPUs: Engines for Future High-Performance Computing

October 6th, 2004

This talk by John Owens of UC Davis discusses trends in GPU architecture and their current and potential uses for high-performance computing. The invited talk was given at the Eighth Annual Workshop on High-Performance Embedded Computing (HPEC 2004). (GPUs: Engines for Future High-Performance Computing)

Room Acoustics Computation on Graphics Hardware

October 6th, 2004

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.)

BionicFX uses GPU as Powerful Audio Effect Processor

September 5th, 2004

From a press release at www.BionicFX.com: “BionicFX announces a revolutionary technology for music production that turns NVIDIA video cards into audio effects processors. Audio Video Exchange (AVEX) converts digital audio into graphics data, and then performs effect calculations using the 3D architecture of the GPU. The latest video cards from NVIDIA are capable of more than 40 gigaflops of processing power compared to less than 6 gigaflops on Intel and AMD CPUs. AVEX represents a major technological achievement that allows music hobbyists and professional artists to run studio quality audio effects at high sample rates on their desktop computer. (Press Release: “Revolutionary Programming and Innovation uses GPU as Powerful Audio Effect Processor”)

Page 94 of 105« First...102030...9293949596...100...Last »