89,647 research outputs found
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Cube-3: A Real-Time Architecture for High-Resolution Volume Visualization
This paper describes a high-performance special-purpose
system, Cube-3, for displaying and manipulating high-
resolution volumetric datasets in real-time. A primary
goal of Cube-3 is to render 512^3, 16-bit per voxel,
datasets at about 30 frames per second. Cube-3 implements a ray-casting algorithm in a highly-parallel and
pipelined architecture, using a 3D skewed volume memory, a modular fast bus, 2D skewed bu ffers, 3D interpolation and shading units, and a ray projection cone.
Cube-3 will allow users to interactively visualize and
investigate in real-time static (3D) and dynamic (4D)
high-resolution volumetric datasets.Engineering and Applied Science
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Towards a Scalable Architecture for Real-Time Volume Rendering
In this paper we present our research eff orts towards a
scalable volume rendering architecture for the real-time
visualization of dynamically changing high-resolution
datasets. Using a linearly skewed memory interleaving we were able to develop a parallel data
ow model
that leads to local, fixed-bandwidth interconnections between processing elements. This parallel dataflow model
diff ers from previous work in that it requires no global
communication of data except at the pixel level. Using this data
ow model we are developing Cube-4, an
architecture that is scalable to very high performances
and allows for modular and extensible hardware implementations.Engineering and Applied Science
On the design of a real-time volume rendering engine
An architecture for a Real-Time Volume Rendering Engine (RT-VRE) is given, capable of computing 750 × 750 × 512 samples from a 3D dataset at a rate of 25 images per second. The RT-VRE uses for this purpose 64 dedicated rendering chips, cooperating with 16 RISC-processors. A plane interpolator circuit and a composition circuit, both capable to operate at very high speeds, have been designed for a 1.6 micron VLSI process. Both the interpolator and composition circuit are back from production. They have been tested and both complied with our specifications
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
Real-Time analysis and visualization for single-molecule based super-resolution microscopy
Accurate multidimensional localization of isolated fluorescent emitters is a time consuming process in single-molecule based super-resolution microscopy. We demonstrate a functional method for real-time reconstruction with automatic feedback control, without compromising the localization accuracy. Compatible with high frame rates of EM-CCD cameras, it relies on a wavelet segmentation algorithm, together with a mix of CPU/GPU implementation. A combination with Gaussian fitting allows direct access to 3D localization. Automatic feedback control ensures optimal molecule density throughout the acquisition process. With this method, we significantly improve the efficiency and feasibility of localization-based super-resolution microscopy
Scalable Interactive Volume Rendering Using Off-the-shelf Components
This paper describes an application of a second generation implementation of the Sepia architecture (Sepia-2) to interactive volu-metric visualization of large rectilinear scalar fields. By employingpipelined associative blending operators in a sort-last configuration a demonstration system with 8 rendering computers sustains 24 to 28 frames per second while interactively rendering large data volumes (1024x256x256 voxels, and 512x512x512 voxels). We believe interactive performance at these frame rates and data sizes is unprecedented. We also believe these results can be extended to other types of structured and unstructured grids and a variety of GL rendering techniques including surface rendering and shadow map-ping. We show how to extend our single-stage crossbar demonstration system to multi-stage networks in order to support much larger data sizes and higher image resolutions. This requires solving a dynamic mapping problem for a class of blending operators that includes Porter-Duff compositing operators
Big Data and Analysis of Data Transfers for International Research Networks Using NetSage
Modern science is increasingly data-driven and collaborative in nature. Many scientific disciplines, including genomics, high-energy physics, astronomy, and atmospheric science, produce petabytes of data that must be shared with collaborators all over the world. The National Science Foundation-supported International Research Network Connection (IRNC) links have been essential to enabling this collaboration, but as data sharing has increased, so has the amount of information being collected to understand network performance. New capabilities to measure and analyze the performance of international wide-area networks are essential to ensure end-users are able to take full advantage of such infrastructure for their big data applications. NetSage is a project to develop a unified, open, privacy-aware network measurement, and visualization service to address the needs of monitoring today's high-speed international research networks. NetSage collects data on both backbone links and exchange points, which can be as much as 1Tb per month. This puts a significant strain on hardware, not only in terms storage needs to hold multi-year historical data, but also in terms of processor and memory needs to analyze the data to understand network behaviors. This paper addresses the basic NetSage architecture, its current data collection and archiving approach, and details the constraints of dealing with this big data problem of handling vast amounts of monitoring data, while providing useful, extensible visualization to end users
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