10,623 research outputs found
Distributed interactive ray tracing for large volume visualization
Journal ArticleWe have constructed a distributed parallel ray tracing system that interactively produces isosurface renderings from large data sets on a cluster of commodity PCs. The program was derived from the SCI Institute's interactive ray tracer (*-Ray), which utilizes small to large shared memory platforms, such as the SGI Origin series, to interact with very large-scale data sets. Making this approach work efficiently on a cluster requires attention to numerous system-level issues, especially when rendering data sets larger than the address space of each cluster node
Interactive ray tracing for volume visualization
Journal ArticleWe present a brute-force ray tracing system for interactive volume visualization, The system runs on a conventional (distributed) shared-memory multiprocessor machine. For each pixel we trace a ray through a volume to compute the color for that pixel. Although this method has high intrinsic computational cost, its simplicity and scalability make it ideal for large datasets on current high-end parallel systems
Interactive Visualization of the Largest Radioastronomy Cubes
3D visualization is an important data analysis and knowledge discovery tool,
however, interactive visualization of large 3D astronomical datasets poses a
challenge for many existing data visualization packages. We present a solution
to interactively visualize larger-than-memory 3D astronomical data cubes by
utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the
data volume into smaller sub-volumes that are distributed over the rendering
workstations. A GPU-based ray casting volume rendering is performed to generate
images for each sub-volume, which are composited to generate the whole volume
output, and returned to the user. Datasets including the HI Parkes All Sky
Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26
GB) data cubes were used to demonstrate our framework's performance. The
framework can render the GASS data cube with a maximum render time < 0.3 second
with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8
GPUs. Our framework will scale to visualize larger datasets, even of Terabyte
order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201
Volume visualization of time-varying data using parallel, multiresolution and adaptive-resolution techniques
This paper presents a parallel rendering approach that allows high-quality visualization of large time-varying volume datasets. Multiresolution and adaptive-resolution techniques are also incorporated to improve the efficiency of the rendering. Three basic steps are needed to implement this kind of an application. First we divide the task through decomposition of data. This decomposition can be either temporal or spatial or a mix of both. After data has been divided, each of the data portions is rendered by a separate processor to create sub-images or frames. Finally these sub-images or frames are assembled together into a final image or animation. After developing this application, several experiments were performed to show that this approach indeed saves time when a reasonable number of processors are used. Also, we conclude that the optimal number of processors is dependent on the size of the dataset used
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
Pycortex: an interactive surface visualizer for fMRI.
Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software
GPU-Based Volume Rendering of Noisy Multi-Spectral Astronomical Data
Traditional analysis techniques may not be sufficient for astronomers to make
the best use of the data sets that current and future instruments, such as the
Square Kilometre Array and its Pathfinders, will produce. By utilizing the
incredible pattern-recognition ability of the human mind, scientific
visualization provides an excellent opportunity for astronomers to gain
valuable new insight and understanding of their data, particularly when used
interactively in 3D. The goal of our work is to establish the feasibility of a
real-time 3D monitoring system for data going into the Australian SKA
Pathfinder archive.
Based on CUDA, an increasingly popular development tool, our work utilizes
the massively parallel architecture of modern graphics processing units (GPUs)
to provide astronomers with an interactive 3D volume rendering for
multi-spectral data sets. Unlike other approaches, we are targeting real time
interactive visualization of datasets larger than GPU memory while giving
special attention to data with low signal to noise ratio - two critical aspects
for astronomy that are missing from most existing scientific visualization
software packages. Our framework enables the astronomer to interact with the
geometrical representation of the data, and to control the volume rendering
process to generate a better representation of their datasets.Comment: 4 pages, 1 figure, to appear in the proceedings of ADASS XIX, Oct 4-8
2009, Sapporo, Japan (ASP Conf. Series
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