high performance computing on graphics processing units: hgpu.org

The simulation of complex layers of folds of cloth can be handled through algorithms which take the physical dynamics into account. In many cases, however, it is sufficient to generate wrinkles on a piece of garment which mostly appears spread out. This paper presents a corresponding fully GPU-based, easy-to-control, and robust method to generate and […]

Existing methods that are able to interactively render complex CSG objects with the aid of GPUs are both image based and severely bandwidth limited. In this paper we present a new approach to this problem whose main advantage is its capability to efficiently scale the dependency on CPU instruction throughput, memory bandwidth and GPU instruction […]

Large and complex systems of ordinary differential equations (ODEs) arise in diverse areas of science and engineering, and pose special challenges on a streaming processor owing to the large amount of state they manipulate. We describe a set of domain-specific source transformations on CUDA C that improved performance by x6.7 on a system of ODEs […]

Recent developments in processing devices such as graphical processing units and multi-core systems offer opportunities to make use of parallel techniques at the chip level to obtain high performance. We discuss the difficulties in establishing suitable benchmark codes for making comparisons across these device architectures and in a way that is representative of key applications. […]

Molecular dynamics simulations are today a widelyused tool in many research fields. Such simulations produce large time-dependent data sets, whichneed to be interactively visualised allowing efficient exploration. On the other hand, commonlyused point-based rendering of the individual particles usually fails to emphasise global contiguousstructures like particle clusters. To solve this issue,we want to visualise these […]

We present a real time vision system designed for and implemented on a graphics processing unit (GPU). After an introduction in GPU programming we describe the architecture of the system and software running on the GPU. We show the advantages of implementing a vision processor on the GPU rather than on a CPU as well […]

Medical image registration tasks of large volume datasets, especially in the non-rigid case, often put a heavy burden on computing resources. GPUs are a promising new approach to address computational intensive image processing tasks. We investigate recently introduced GPU hardware features that accelerate 2D and 3D rigid and nonrigid registration tasks. Our implementation is entirely […]

Improving the visual appearance of coarse triangle meshes is usually done with graphics hardware with per-pixel shading techniques. Improving the appearance at silhouettes is inherently hard, as shading has only a small influence there and the geometry must be corrected. With the new geometry shader stage released with DirectX 10, the functionality to generate new […]

The availability of low cost powerful parallel graphics cards has stimulated a trend to port GP on Graphics Processing Units (GPUs). Previous works on GPUs have shown evaluation phase speedups for large training cases sets. Using the CUDA language on the G80 GPU, we show it is possible to efficiently interpret several GP programs in […]

Mackey-Glass chaotic time series prediction and nuclear protein classification show the feasibility of evaluating genetic programming populations directly on parallel consumer gaming graphics processing units. Using a Linux KDE computer equipped with an nVidia GeForce 8800 GTX graphics processing unit card the C++ SPMD interpretter evolves programs at Giga GP operations per second (895 million […]

In this paper, we propose a novel simulation algorithm for large scale structured power grid networks. The new method formulates the traditional linear system as a special two-dimension Poisson equation and solves it using an analytical expressions based on FFT technique. The computation complexity of the new algorithm is O(NlgN), which is much smaller than […]

We present a novel GPU level set segmentation algorithm that is both work-efficient and step-efficient. Our algorithm has O(log n) step-complexity, in contrast to previous GPU algorithms [Lefohn et al. 2004; Jeong et al. 2009] which have O(n) step-complexity. Moreover our algorithm limits the active computational domain to the minimal set of changing elements by […]