In this poster we propose to interconnect LAMMPS and vl3, our parallel framework for large-scalevisualization and analysis. The goal is co-visualization and exploration at interactive frame rates of large-scale atomi...
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ISBN:
(纸本)9781467385176
In this poster we propose to interconnect LAMMPS and vl3, our parallel framework for large-scalevisualization and analysis. The goal is co-visualization and exploration at interactive frame rates of large-scale atomistic simulations. We describe the architecture of the proposed system, as well as a preliminary implementation that runs on GPU-based visualization clusters.
This application paper presents a visual analytics tool designed to explore large-scale scientific data modeled after a natural climate phenomenon. The data are modeled on a high-performance computer and exported to a...
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ISBN:
(纸本)9781479952151
This application paper presents a visual analytics tool designed to explore large-scale scientific data modeled after a natural climate phenomenon. The data are modeled on a high-performance computer and exported to a personal computer for interactive visualization. The system is co-designed by visual analytics researchers and domain scientists after a year of rapid prototyping and evaluation of multiple information and scientific visualization techniques using a model dataset that includes both scalar fields and flow fields. Five information-visualization and one scientific-visualization techniques are included in the visual analytics system to balance analytical effectiveness and computation time for large-scale interactive exploration. The paper discusses the system design, explains the design rationale, and shares computation performance and results of different visualization techniques. The primary contribution of this application paper is to show that we can interactively and effectively visualize a large amount of scientific model data on a modest desktop computer. The computation performance results of the individual visualization techniques and the overall system also provide benchmark references for other large-scalevisualization development efforts.
This paper details the conception of a remote and powerful visualization tool intended to be directly used by physicists in order to better understand the physical phenomena in the field of fast dynamics simulations. ...
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ISBN:
(纸本)9781479952151
This paper details the conception of a remote and powerful visualization tool intended to be directly used by physicists in order to better understand the physical phenomena in the field of fast dynamics simulations. The goal is to explore largedataset composed of billions of cells distributed in thousands of domains on a supercomputer. The solution uses the visualization tool VisIt [1] adapted with specific developments based on the knowledge of both the simulation code and the supercomputer architecture used to run the simulations.
large-scale scientific simulations typically output massive amounts of data that must be later read in for post-hoc visualization and analysis. With codes simulating complex phenomena at ever-increasing fidelity, writ...
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ISBN:
(纸本)9781665432832
large-scale scientific simulations typically output massive amounts of data that must be later read in for post-hoc visualization and analysis. With codes simulating complex phenomena at ever-increasing fidelity, writing data to disk during this traditional high-performance computing workflow has become a significant bottleneck. In situ workflows offer a solution to this bottleneck, whereby data is simultaneously produced and analyzed without involving disk storage. In situ analysis can increase efficiency for domain scientists who are exploring a data set or fine-tuning visualization and analysis parameters. Our work seeks to enable researchers to easily create and interactively analyze large-scale simulations through the use of Jupyter Notebooks without requiring application developers to explicitly integrate in situ libraries.
Volumetric grids have recently been used by many recent works for representing complex scenes implicitly. A volumetric neural representation can be several orders of magnitude smaller in size while still preserving mo...
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ISBN:
(数字)9781665491563
ISBN:
(纸本)9781665491563
Volumetric grids have recently been used by many recent works for representing complex scenes implicitly. A volumetric neural representation can be several orders of magnitude smaller in size while still preserving most of high-frequency details. However, most volumes used in large-scale in situ visualization and analysis are partitioned and generated directly in parallel. Therefore, a compatible technique to create volumetric neural representations for these situations is much needed. In this project, we explore the possibility of constructing and optimizing such a representation for large-scale distributed volumes. We present our preliminary results in this poster. We also outline our plans to integrate our techniques with existing in situ visualization and analysis pipelines.
We describe the design and deployment of Many Eyes, a public web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around v...
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We describe the design and deployment of Many Eyes, a public web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around visualizations at a largescale by fostering a social style of dataanalysis in which visualizations not only serve as a discovery tool for individuals but also as a medium to spur discussion among users. To support this goal, the site includes novel mechanisms for end-user creation of visualizations and asynchronous collaboration around those visualizations. In addition to describing these technologies, we provide a preliminary report on the activity of our users.
In this project we explore several different techniques for visualizing data created by Hardware/Hybrid Accelerated Cosmology Code (HACC) cosmology simulations. We present four methods that have thus far been explored...
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ISBN:
(纸本)9781538668733
In this project we explore several different techniques for visualizing data created by Hardware/Hybrid Accelerated Cosmology Code (HACC) cosmology simulations. We present four methods that have thus far been explored. Mainly, we discuss visualizing data through ParaView, vl3, SPH interpolation, and virtual reality environments. We display our preliminary results, as well as our plans to begin applying our techniques to largescaledata sets.
For user to analyze largedata set with various attributes as desired, rearranging data based on purpose is vital. This study proposes a method to navigate data with a desired purpose of user by improving RadViz visua...
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ISBN:
(纸本)9781538668733
For user to analyze largedata set with various attributes as desired, rearranging data based on purpose is vital. This study proposes a method to navigate data with a desired purpose of user by improving RadViz visualization through focusing and filtering. To help understand visualization, user studies were conducted using the music data of Spotify. As a result, our system is discovered to be effective at classifying largedata set of music with attributes and navigating desired music efficiently.
Point set visualization is required in lots of visualization techniques. Scatter plots as well as geographic heat-maps are straightforward examples. data analysts are now well trained to use such visualization techniq...
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ISBN:
(纸本)9781467385176
Point set visualization is required in lots of visualization techniques. Scatter plots as well as geographic heat-maps are straightforward examples. data analysts are now well trained to use such visualization techniques. The availability of larger and larger datasets raises the need to make these techniques scale as fast as the data grows. The Big data Infrastructure offers the possibility to scale horizontally. Designing point set visualization methods that fit into that new paradigm is thus a crucial challenge. In this paper, we present a complete architecture which fully fits into the Big data paradigm and so enables interactive visualization of heatmaps at ultra-scale. A new distributed algorithm for multi-scale aggregation of point set is given and an adaptive GPU based method for kernel density estimation is proposed. A complete prototype working with Hadoop, HBase, Spark and WebGL has been implemented. We give a benchmark of our solution on a dataset having more than 2 billion points.
Recent advances in large-scale simulations have resulted in volume data of increasing size that stress the capabilities of off-the-shelf visualization tools. Users suffer from long data loading times, because large da...
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ISBN:
(纸本)9781728126050
Recent advances in large-scale simulations have resulted in volume data of increasing size that stress the capabilities of off-the-shelf visualization tools. Users suffer from long data loading times, because largedata must be read from disk into memory prior to rendering the first frame. In this work, we present a volume renderer that enables high-fidelity interactive visualization of large volumes on multi-core CPU architectures. Compared to existing CPU-based visualization frameworks, which take minutes or hours for data loading, our renderer allows users to get a data overview in seconds. Using a hierarchical representation of raw volumes and ray-guided streaming. we reduce the data loading time dramatically and improve the user's interactivity experience. We also examine system design choices with respect to performance and scalability. Specifically, we evaluate the hierarchy generation time, which has been ignored in most prior work, but which can become a significant bottleneck as datascales. Finally, we create a module on top of the OSPRay ray tracing framework that is ready to be integrated into general-purpose visualization frameworks such as Paraview.
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