2,705 research outputs found
Direct volume rendering of unstructured tetrahedral meshes using CUDA and OpenMP
Cataloged from PDF version of article.Direct volume visualization is an important method in many areas, including computational fluid dynamics and medicine. Achieving interactive rates for direct volume rendering of large unstructured volumetric grids is a challenging problem, but parallelizing direct volume rendering algorithms can help achieve this goal. Using Compute Unified Device Architecture (CUDA), we propose a GPU-based volume rendering algorithm that itself is based on a cell projection-based ray-casting algorithm designed for CPU implementations. We also propose a multicore parallelized version of the cell-projection algorithm using OpenMP. In both algorithms, we favor image quality over rendering speed. Our algorithm has a low memory footprint, allowing us to render large datasets. Our algorithm supports progressive rendering. We compared the GPU implementation with the serial and multicore implementations. We observed significant speed-ups that, together with progressive rendering, enables reaching interactive rates for large datasets
Volumetric rendering techniques for scientific visualization
Ankara : The Department of Computer Engineering and The Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Ph.D.) -- Bilkent University, 2014.Includes bibliographical references leaves 80-86.Direct volume rendering is widely used in many applications where the inside of a
transparent or a partially transparent material should be visualized. We have explored
several aspects of the problem. First, we proposed a view-dependent selective refinement
scheme in order to reduce the high computational requirements without affecting
the image quality significantly. Then, we explored the parallel implementations of
direct volume rendering: both on GPU and on multi-core systems. Finally, we used direct
volume rendering approaches to create a tool, MaterialVis, to visualize amorphous
and/or crystalline materials.
Visualization of large volumetric datasets has always been an important problem.
Due to the high computational requirements of volume-rendering techniques, achieving
interactive rates is a real challenge. We present a selective refinement scheme
that dynamically refines the mesh according to the camera parameters. This scheme
automatically determines the impact of different parts of the mesh on the output image
and refines the mesh accordingly, without needing any user input. The viewdependent
refinement scheme uses a progressive mesh representation that is based
on an edge collapse-based tetrahedral mesh simplification algorithm. We tested our
view-dependent refinement framework on an existing state-of-the-art volume renderer.
Thanks to low overhead dynamic view-dependent refinement, we achieve interactive
frame rates for rendering common datasets at decent image resolutions.
Achieving interactive rates for direct volume rendering of large unstructured volumetric
grids is a challenging problem, but parallelizing direct volume rendering algorithms
can help achieve this goal. Using Compute Unified Device Architecture
(CUDA), we propose a GPU-based volume rendering algorithm that itself is based on
a cell projection-based ray-casting algorithm designed for CPU implementations. We
also propose a multi-core parallelized version of the cell-projection algorithm using OpenMP. In both algorithms, we favor image quality over rendering speed. Our algorithm
has a low memory footprint, allowing us to render large datasets. Our algorithm
support progressive rendering. We compared the GPU implementation with the serial
and multi-core implementations. We observed significant speed-ups, that, together
with progressive rendering, enabling reaching interactive rates for large datasets.
Visualization of materials is an indispensable part of their structural analysis. We
developed a visualization tool for amorphous as well as crystalline structures, called
MaterialVis. Unlike the existing tools, MaterialVis represents material structures as a
volume and a surface manifold, in addition to plain atomic coordinates. Both amorphous
and crystalline structures exhibit topological features as well as various defects.
MaterialVis provides a wide range of functionality to visualize such topological structures
and crystal defects interactively. Direct volume rendering techniques are used
to visualize the volumetric features of materials, such as crystal defects, which are
responsible for the distinct fingerprints of a specific sample. In addition, the tool provides
surface visualization to extract hidden topological features within the material.
Together with the rich set of parameters and options to control the visualization, MaterialVis
allows users to visualize various aspects of materials very efficiently as generated
by modern analytical techniques such as the Atom Probe Tomography.Okuyan, ErhanPh.D
From Big Data to Big Displays: High-Performance Visualization at Blue Brain
Blue Brain has pushed high-performance visualization (HPV) to complement its
HPC strategy since its inception in 2007. In 2011, this strategy has been
accelerated to develop innovative visualization solutions through increased
funding and strategic partnerships with other research institutions.
We present the key elements of this HPV ecosystem, which integrates C++
visualization applications with novel collaborative display systems. We
motivate how our strategy of transforming visualization engines into services
enables a variety of use cases, not only for the integration with high-fidelity
displays, but also to build service oriented architectures, to link into web
applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho
Interactive inspection of complex multi-object industrial assemblies
The final publication is available at Springer via http://dx.doi.org/10.1016/j.cad.2016.06.005The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution–based on the analysis of several existing view-dependent visualization schemes–uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.Peer ReviewedPostprint (author's final draft
Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences
Results: We present an application that enables the quantitative analysis of
multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence
microscopy images. The image sequences show stem cells together with blood
vessels, enabling quantification of the dynamic behaviors of stem cells in
relation to their vascular niche, with applications in developmental and cancer
biology. Our application automatically segments, tracks, and lineages the image
sequence data and then allows the user to view and edit the results of
automated algorithms in a stereoscopic 3-D window while simultaneously viewing
the stem cell lineage tree in a 2-D window. Using the GPU to store and render
the image sequence data enables a hybrid computational approach. An
inference-based approach utilizing user-provided edits to automatically correct
related mistakes executes interactively on the system CPU while the GPU handles
3-D visualization tasks. Conclusions: By exploiting commodity computer gaming
hardware, we have developed an application that can be run in the laboratory to
facilitate rapid iteration through biological experiments. There is a pressing
need for visualization and analysis tools for 5-D live cell image data. We
combine accurate unsupervised processes with an intuitive visualization of the
results. Our validation interface allows for each data set to be corrected to
100% accuracy, ensuring that downstream data analysis is accurate and
verifiable. Our tool is the first to combine all of these aspects, leveraging
the synergies obtained by utilizing validation information from stereo
visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc
Physics-based visual characterization of molecular interaction forces
Molecular simulations are used in many areas of biotechnology, such as drug design and enzyme engineering. Despite the development of automatic computational protocols, analysis of molecular interactions is still a major aspect where human comprehension and intuition are key to accelerate, analyze, and propose modifications to the molecule of interest. Most visualization algorithms help the users by providing an accurate depiction of the spatial arrangement: the atoms involved in inter-molecular contacts. There are few tools that provide visual information on the forces governing molecular docking. However, these tools, commonly restricted to close interaction between atoms, do not consider whole simulation paths, long-range distances and, importantly, do not provide visual cues for a quick and intuitive comprehension of the energy functions (modeling intermolecular interactions) involved. In this paper, we propose visualizations designed to enable the characterization of interaction forces by taking into account several relevant variables such as molecule-ligand distance and the energy function, which is essential to understand binding affinities. We put emphasis on mapping molecular docking paths obtained from Molecular Dynamics or Monte Carlo simulations, and provide time-dependent visualizations for different energy components and particle resolutions: atoms, groups or residues. The presented visualizations have the potential to support domain experts in a more efficient drug or enzyme design process.Peer ReviewedPostprint (author's final draft
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