The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware. We begin 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. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware. This survey should be of particular interest to researchers who are interested in using the latest GPGPU applications in their systems of interest.

/pdf/a-survey-of-general-purpose-computation-on-graphics-hardware-2phx1q9xxr.pdf

A Survey of General-Purpose Computation on Graphics Hardware

A Survey of General-Purpose Computation on Graphics Hardware.

We present a new approach for computing generalized 2D and 3D Voronoi diagrams using interpolation-based polygon rasterization hardware. We compute a discrete Voronoi diagram by rendering a three dimensional distance mesh for each Voronoi site. The polygonal mesh is a bounded-error approximation of a (possibly) non-linear function of the distance between a site and a 2D planar grid of sample points. For each sample point, we compute the closest site and the distance to that site using polygon scan-conversion and the Z-buffer depth comparison. We construct distance meshes for points, line segments, polygons, polyhedra, curves, and curved surfaces in 2D and 3D. We generalize to weighted and farthest-site Voronoi diagrams, and present efficient techniques for computing the Voronoi boundaries, Voronoi neighbors, and the Delaunay triangulation of points. We also show how to adaptively refine the solution through a simple windowing operation. The algorithm has been implemented on SGI workstations and PCs using OpenGL, and applied to complex datasets. We demonstrate the application of our algorithm to fast motion planning in static and dynamic environments, selection in complex user-interfaces, and creation of dynamic mosaic effects. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; I.3.3 [Computer Graphics]: Picture/Image Generation. Additional

/pdf/fast-computation-of-generalized-voronoi-diagrams-using-35wpydj9nw.pdf

Fast computation of generalized Voronoi diagrams using graphics hardware

The graphics processor (GPU) on today's commodity video cards has evolved into an extremely powerful and flexible processor. The latest graphics architectures provide tremendous memory bandwidth and computational horsepower, with fully programmable vertex and pixel processing units that support vector operations up to full IEEE floating point precision. High level languages have emerged for graphics hardware, making this computational power accessible. Architecturally, GPUs are highly parallel streaming processors optimized for vector operations, with both MIMD (vertex) and SIMD (pixel) pipelines. Not surprisingly, these processors are capable of general-purpose computation beyond the graphics applications for which they were designed. Researchers have found that exploiting the GPU can accelerate some problems by over an order of magnitude over the CPU.However, significant barriers still exist for the developer who wishes to use the inexpensive power of commodity graphics hardware, whether for in-game simulation of physics of for conventional computational science. These chips are designed for and driven by video game development; the programming model is unusual, the programming environment is tightly constrained, and the underlying architectures are largely secret. The GPU developer must be an expert in computer graphics and its computational idioms to make effective use of the hardware, and still pitfalls abound. This course provides a detailed introduction to general purpose computation on graphics hardware (GPGPU). We emphasize core computational building blocks, ranging from linear algebra to database queries, and review the tools, perils, and tricks of the trade in GPU programming. Finally we present some interesting and important case studies on general-purpose applications of graphics hardware.The course presenters are experts on general-purpose GPU computation from academia and industry, and have presented papers and tutorials on the topic at SIGGRAPH, Graphics Hardware, Game Developers Conference, and elsewhere.

GPGPU: general purpose computation on graphics hardware

To reduce the cost of correcting design errors, assemblies of mechanical parts are modeled using CAD systems and verified electronically before the designs are sent to manufacturing. Shaded images are insufficient for examining the internal structures of assemblies and for detecting interferences. Thus, designers must rely on expensive numerical techniques that compute geometric representations of cross-sections and of intersections of solids. The solid-clipping approach presented here bypasses these geometric calculations and offers realtime rendering of cross-sections and interferences for solids represented by their facetted boundaries. In its simplest form, the technique is supported by contemporary highend graphics workstations. Its variations, independently developed elsewhere, have already been demonstrated. Our implementation is based on the concept of a cutvolume interactively manipulated to remove obstructing portions of the assembly and reveal its internal structure. For clarity, faces of the cut-volume which intersect a single solid are hatched and shaded with the color of that solid. Interference areas between two or more solids are highlighted. Furthermore, to help users find the first occurrence of an interference along a search direction, we have developed an adaptive subdivision search based on a projective approach which guarantees a sufficient condition for object disjointness. The additional performance cost for solid-clipping and interference highlighting is comparable to the standard rendering cost. An efficient implementation of the disjointness test requires a minor extension of the graphics functions currently supported on commercial hardware.

https://www.cc.gatech.edu/~jarek/papers/Capping.pdf

Abe Megahed

Papers

Interactive inspection of solids: cross-sections and interferences