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Graphics Processing Units (graphics + processing_unit)
Selected AbstractsPractical CFD Simulations on Programmable Graphics Hardware using SMAC,COMPUTER GRAPHICS FORUM, Issue 4 2005Carlos E. Scheidegger Abstract The explosive growth in integration technology and the parallel nature of rasterization-based graphics APIs (Application Programming Interface) changed the panorama of consumer-level graphics: today, GPUs (Graphics Processing Units) are cheap, fast and ubiquitous. We show how to harness the computational power of GPUs and solve the incompressible Navier-Stokes fluid equations significantly faster (more than one order of magnitude in average) than on CPU solvers of comparable cost. While past approaches typically used Stam's implicit solver, we use a variation of SMAC (Simplified Marker and Cell). SMAC is widely used in engineering applications, where experimental reproducibility is essential. Thus, we show that the GPU is a viable and affordable processor for scientific applications. Our solver works with general rectangular domains (possibly with obstacles), implements a variety of boundary conditions and incorporates energy transport through the traditional Boussinesq approximation. Finally, we discuss the implications of our solver in light of future GPU features, and possible extensions such as three-dimensional domains and free-boundary problems. [source] Multilevel fast multipole algorithm enhanced by GPU parallel technique for electromagnetic scattering problemsMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 3 2010Kan Xu Abstract Along with the development of graphics processing Units (GPUS) in floating point operations and programmability, GPU has increasingly become an attractive alternative to the central processing unit (CPU) for some of compute-intensive and parallel tasks. In this article, the multilevel fast multipole algorithm (MLFMA) combined with graphics hardware acceleration technique is applied to analyze electromagnetic scattering from complex target. Although it is possible to perform scattering simulation of electrically large targets on a personal computer (PC) through the MLFMA, a large CPU time is required for the execution of aggregation, translation, and deaggregation operations. Thus GPU computing technique is used for the parallel processing of MLFMA and a significant speedup of matrix vector product (MVP) can be observed. Following the programming model of compute unified device architecture (CUDA), several kernel functions characterized by the single instruction multiple data (SIMD) mode are abstracted from components of the MLFMA and executed by multiple processors of the GPU. Numerical results demonstrate the efficiency of GPU accelerating technique for the MLFMA. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 502,507, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24963 [source] Interactive visualization of quantum-chemistry dataACTA CRYSTALLOGRAPHICA SECTION A, Issue 5 2010Yun Jang Simulation and computation in chemistry studies have improved as computational power has increased over recent decades. Many types of chemistry simulation results are available, from atomic level bonding to volumetric representations of electron density. However, tools for the visualization of the results from quantum-chemistry computations are still limited to showing atomic bonds and isosurfaces or isocontours corresponding to certain isovalues. In this work, we study the volumetric representations of the results from quantum-chemistry computations, and evaluate and visualize the representations directly on a modern graphics processing unit without resampling the result in grid structures. Our visualization tool handles the direct evaluation of the approximated wavefunctions described as a combination of Gaussian-like primitive basis functions. For visualizations, we use a slice-based volume-rendering technique with a two-dimensional transfer function, volume clipping and illustrative rendering in order to reveal and enhance the quantum-chemistry structure. Since there is no need to resample the volume from the functional representations for the volume rendering, two issues, data transfer and resampling resolution, can be ignored; therefore, it is possible to explore interactively a large amount of different information in the computation results. [source] GPU-accelerated boundary element method for Helmholtz' equation in three dimensionsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 10 2009Toru Takahashi Abstract Recently, the application of graphics processing units (GPUs) to scientific computations is attracting a great deal of attention, because GPUs are getting faster and more programmable. In particular, NVIDIA's GPUs called compute unified device architecture enable highly mutlithreaded parallel computing for non-graphic applications. This paper proposes a novel way to accelerate the boundary element method (BEM) for three-dimensional Helmholtz' equation using CUDA. Adopting the techniques for the data caching and the double,single precision floating-point arithmetic, we implemented a GPU-accelerated BEM program for GeForce 8-series GPUs. The program performed 6,23 times faster than a normal BEM program, which was optimized for an Intel's quad-core CPU, for a series of boundary value problems with 8000,128000 unknowns, and it sustained a performance of 167,Gflop/s for the largest problem (1 058 000 unknowns). The accuracy of our BEM program was almost the same as that of the regular BEM program using the double precision floating-point arithmetic. In addition, our BEM was applicable to solve realistic problems. In conclusion, the present GPU-accelerated BEM works rapidly and precisely for solving large-scale boundary value problems for Helmholtz' equation. Copyright © 2009 John Wiley & Sons, Ltd. [source] Parallel, distributed and GPU computing technologies in single-particle electron microscopyACTA CRYSTALLOGRAPHICA SECTION D, Issue 7 2009Martin Schmeisser Most known methods for the determination of the structure of macromolecular complexes are limited or at least restricted at some point by their computational demands. Recent developments in information technology such as multicore, parallel and GPU processing can be used to overcome these limitations. In particular, graphics processing units (GPUs), which were originally developed for rendering real-time effects in computer games, are now ubiquitous and provide unprecedented computational power for scientific applications. Each parallel-processing paradigm alone can improve overall performance; the increased computational performance obtained by combining all paradigms, unleashing the full power of today's technology, makes certain applications feasible that were previously virtually impossible. In this article, state-of-the-art paradigms are introduced, the tools and infrastructure needed to apply these paradigms are presented and a state-of-the-art infrastructure and solution strategy for moving scientific applications to the next generation of computer hardware is outlined. [source] Anwendung von massiv paralleler Berechnung mit Grafikkarten (GPGPU) für CFD-Methoden im BrandschutzBAUPHYSIK, Issue 4 2009Hendrik C. Belaschk Dipl.-Ing. Berechnungsverfahren; Brandschutz; calculation methods; fire protection engineering Abstract Der Einsatz von Brandsimulationsprogrammen, die auf den Methoden der Computational Fluid Dynamics (CFD) beruhen, wird in der Praxis immer breiter. Infolge der Zunahme von verfügbarer Rechenleistung in der Computertechnik können heute die Auswirkungen möglicher Brandszenarien nachgebildet und daraus nützliche Informationen für den Anwendungsfall gewonnen werden (z. B. Nachweis der Zuverlässigkeit von Brandschutzkonzepten). Trotz der erzielten Fortschritte reicht die Leistung von heute verfügbaren Computern bei weitem nicht aus, um einen Gebäudebrand mit allen beteiligten physikalischen und chemischen Prozessen mit der höchstmöglichen Genauigkeit zu simulieren. Die in den Computerprogrammen zur Berechnung der Brand- und Rauchausbreitung implementierten Modelle stellen daher immer einen Kompromiss zwischen der praktischen Recheneffizienz und dem Detailgrad der Modellierung dar. Im folgenden Aufsatz wird gezeigt, worin die Ursachen für den hohen Rechenbedarf der CFD-Methoden liegen und welche Problemstellungen und möglichen Fehlerquellen sich aus den getroffenen Modellvereinfachungen für den Ingenieur ergeben. Darüber hinaus wird ein neuer Technologieansatz vorgestellt, der die Rechenleistung eines Personalcomputers unter Verwendung spezieller Software und handelsüblicher 3D-Grafikkarten massiv erhöht. Hierzu wird am Beispiel des Fire Dynamics Simulator (FDS) demonstriert, dass sich die erforderliche Berechnungszeit für eine Brandsimulation auf einem Personalcomputer um den Faktor 20 und mehr verringern lässt. Application of general-purpose computing on graphics processing units (GPGPU) in CFD techniques for fire safety simulations. The use of fire simulation programs based on computational fluid dynamics (CFD) techniques is becoming more and more widespread in practice. The increase in available computing power enables the effects of possible fire scenarios to be modelled in order to derive useful information for practical applications (e.g. analysis of the reliability of fire protection concepts). However, despite the progress in computing power the performance of currently available computers is inadequate for simulating a building fire including all relevant physical and chemical processes with maximum accuracy. The models for calculating the spread of fire and smoke implemented in the computer programs therefore always represent a compromise between practical computing efficiency and level of modelling detail. This paper illustrates the reasons for the high computing power demand of CFD techniques and describes potential problems and sources of error resulting from simplifications applied in the models. In addition, the paper presents a new technology approach that significantly increases the computing power of a PC using special software and standard 3D graphics cards. The Fire Dynamics Simulator (FDS) is used as an example to demonstrate how the required calculation time for a fire simulation on a PC can be reduced by a factor of 20 and more. [source] | |||||||||||||