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ieee Pacific visualization symposium (ieee PacificVis)

作者： Zhao, Kun Sakamoto, Naohisa Koyamada, Koji IBM Res Tokyo Tokyo Japan Kobe Univ Kobe Hyogo Japan Kyoto Univ Kyoto Japan

ISBN: (纸本)9781509057382

The primary challenge in the visualization of a large-scale unstructured volume data with mixed cell types is to dissolve a bottleneck caused by visibility sorting of unstructured cells. In this paper, we implement an interactive particle-based rendering method to solve this complex visualization problem. This technique uses opaque particles as the proxy geometry of the mixed unstructured cells so that the visibility sorting process is not needed. This characteristic makes the rendering of mixed-cell unstructured volume efficient. We also construct a resizing function to adjust the particle radius depending on the assigned transfer function so that the transfer function can be adjusted in real time. Furthermore, we develop a time-varying level-of-detail (LOD) rendering to efficiently handle the large-scale time-varying data. This LOD rendering can provide highspeed rendering for animation rendering and high-quality rendering when the animation is stopped at any time step of interest. These features facilitate the detailed analysis of the temporal features of the data.

关键词： unstructured volume large-scale visualization mixed cell time-varying visualization

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Statistical Super Resolution for data analysis and visualization of large Scale Cosmological Simulations 12

Statistical Super Resolution for Data Analysis and Visualiza...

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12th ieee Pacific visualization symposium (ieee PacificVis)

作者： Wang, Ko-Chih Xu, Jiayi Woodring, Jonathan Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781538692264

Cosmologists build simulations for the evolution of the universe using different initial parameters. By exploring the datasets from different simulation runs, cosmologists can understand the evolution of our universe and approach its initial conditions. A cosmological simulation nowadays can generate datasets on the order of petabytes. Moving datasets from the supercomputers to post data analysis machines is infeasible. We propose a novel approach called statistical super-resolution to tackle the big data problem for cosmological data analysis and visualization. It uses datasets from a few simulation runs to create a prior knowledge, which captures the relation between low- and high-resolution data. We apply in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage. High-resolution datasets are reconstructed from the statistical down-sampled data by using the prior knowledge for scientists to perform advanced data analysis and render high-quality visualizations.

关键词： data models data analysis data visualization Analytical models Computational modeling Image resolution Bandwidth

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Parallel Particle Advection and Lagrangian analysis for 3D-PLI Fiber Orientation Maps 9

Parallel Particle Advection and Lagrangian Analysis for 3D-P...

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9th ieee symposium on large data analysis and visualization (LDAV)

作者： Demiralp, Ali C. Zielasko, Daniel Axer, Markus Vierjahn, Tom Kuhlen, Torsten W. Rhein Westfal TH Aachen Visual Comp Inst JARA HPC Aachen Germany Univ Trier Trier Germany Julich Res Ctr Julich Germany Westphalian Univ Appl Sci Gelsenkirchen Germany

ISBN: (纸本)9781728126050

3D-Polarized Light Imaging (3D-PLI) is a recent neuroimaging technique which is able to record the orientation of nerve fibers in unprecedented detail, reaching micrometer scale. Particle advection, which streamline tractography is based upon, is an established tool to study the structure of fiber tracts within the brain. This work presents a distributed particle advection pipeline for 3D-PLI fiber orientation maps, which are time-invariant vector fields describing the dominant orientation of nerve fibers at each point. Due to the high resolution, 3D-PLI creates large datasets ranging from few gigabytes up to hundreds of terabytes, which is addressed through a data-parallel approach to advection. Furthermore, we propose Lagrangian analysis of time-invariant brain data through computation of Finite-Time Lyapunov Exponents (FTLE), which may aid identification of macroscopic fiber bundles.

关键词： D.1.3 [Software]: Programming Techniques Concurrent programming I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling Curve, surface, solid and object representations

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Virtual Reality Tools for the Correction of Automated Volume Segmentation Errors using Dense Surface Reconstructions 7

Virtual Reality Tools for the Correction of Automated Volume...

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ieee 7th symposium on large data analysis and visualization (LDAV)

作者： Brooks, Edouard B. Insley, Joseph Papka, Michael E. Rizzi, Silvio Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Northern Illinois Univ Argonne Natl Lab De Kalb IL 60115 USA

ISBN: (纸本)9781538606179

The Kasthuri laboratory at the University of Chicago seeks to map the human brain and have begun by mapping mouse brains at high resolutions. In 2015, using electron microscopy imaging of miniscule slices, Kasthuri et al. produced a high-resolution volume reconstruction of neocortical tissue roughly the size of 1500 mu m(3): a resolution allowing identification of every synaptic vesicle [2]. Today, the Kasthuri lab has been working with the Argonne National Laboratory to automatically segment and visualize volumes using Argonne's supercomputers. This poster presents initial work on a toolset for neuroanatomists to use to engage with and correct errors associated to the automated segmentation, in anticipation of future, larger volumes. The project leverages commercial virtual reality head-mounted displays (HMDs), and will give scientists the opportunity to make corrections in real time and while disconnected from the supercomputers. The development follows studies that have found that greater field of regard, head tracking, and immersion improves visual analysis and spatial understanding of inherently three dimensional datasets.

关键词： Virtual reality multi-perspective visualization interaction large volume reconstructions surface reconstruction

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Towards Adaptive Refinement for Multivariate Functional Approximation of Scientific data 13

Towards Adaptive Refinement for Multivariate Functional Appr...

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13th ieee symposium on large data analysis and visualization (LDAV)

作者： Peterka, Tom Lenz, David Grindeanu, Iulian Mahadevan, Vijay S. Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9798350330182

We investigate a data model for adaptive refinement in multivariate functional approximation of scientific data, based on a mesh of varying-resolution tensor products, and offering reduced size compared with a single tensor product representation. The mesh of tensor products adjoins in irregular fashion to tessellate the entire domain, with high-degree continuity across tensor product boundaries. The result is that regions of refinement, with additional knots and control points, are localized to individual tensor products rather than extending throughout the entire domain. The model attains similar accuracy with fewer total control points than a single tensor product model, at the cost of added computational complexity to manage the continuity and accuracy across tensor products. We describe our high-dimensional data organization and demonstrate how to approximate scientific datasets using our data model. Size and speed are compared between a single tensor product and our representation. Initial results demonstrate correct functionality and modest reduction in the number of control points required to attain comparable accuracy as a single tensor, with increased computation time as expected. Our initial findings indicate two avenues for future research: additional tuning of the adaptive refinement algorithm to reduce size further, and accelerating computation through parallelism.

关键词： Mathematics of Computing Continuous Mathematics Continuous Functions

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Graphical Representation through a User Interface for In Situ Scientific visualization with Ascent 14

Graphical Representation through a User Interface for In Sit...

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14th symposium on large data analysis and visualization

作者： Heinemann, Colleen Amstutz, Jefferson Insley, Joseph A. Mateevitsi, Victor A. Papka, Michael E. Rizzi, Silvio Univ Illinois Urbana IL 61801 USA Argonne Natl Lab Argonne IL 60439 USA NVIDIA Santa Clara CA USA Univ Illinois Chicago IL USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (纸本)9798331516932;9798331516925

This work presents a graphical user interface (GUI) designed to allow users to interactively create scientific visualizations and workflows with the in situ visualization software, Ascent [5]. Traditionally, in situ pipelines are configured through a YAML file, which can be cumbersome. Our GUI offers an alternative by automating the conversion of user inputs into a format that Ascent can process, eliminating the need for manual file editing. This interactive capability increases the potential for a more diverse group of users to leverage Ascent's powerful visualization tools.

关键词： In situ visualization graphical user interface (GUI) HPC simulations scientific visualization Ascent

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Distributed Percolation analysis for Turbulent Flows 9

Distributed Percolation Analysis for Turbulent Flows

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9th ieee symposium on large data analysis and visualization (LDAV)

作者： Friederici, Anke Kopp, Wiebke Atzori, Marco Vinuesa, Ricardo Schlatter, Philipp Weinkauf, Tino KTH Royal Inst Technol Div Computat Sci & Technol Stockholm Sweden KTH Mech Linne FLOW Ctr Stockholm Sweden

ISBN: (纸本)9781728126050

Percolation analysis is a valuable tool to study the statistical properties of turbulent flows. It is based on computing the percolation function for a derived scalar field, thereby quantifying the relative volume of the largest connected component in a superlevel set for a decreasing threshold. We propose a novel memory-distributed parallel algorithm to finely sample the percolation function. It is based on a parallel version of the union-find algorithm interleaved with a global synchronization step for each threshold sample. The efficiency of this algorithm stems from the fact that operations in-between threshold samples can be freely reordered, are mostly local and thus require no inter-process communication. Our algorithm is significantly faster than previous algorithms for this purpose, and is neither constrained by memory size nor number of compute nodes compared to the conceptually related algorithm for extracting augmented merge trees. This makes percolation analysis much more accessible in a large range of scenarios. We explore the scaling of our algorithm for different data sizes, number of samples and number of MPI processes. We demonstrate the utility of percolation analysis using large turbulent flow data sets.

关键词： Computing methodologies Distributed computing methodologies Distributed algorithms Mathematics of computing Discrete mathematics Graph theory Paths and connectivity problems

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WarpIV: In Situ visualization and analysis of Ion Accelerator Simulations

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2016年第3期36卷 22-35页

作者： Rubel, Oliver Loring, Burlen Vay, Jean-Luc Grote, David P. Lehe, Remi Bulanov, Stepan Vincenti, Henri Bethel, E. Wes Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Div Phys Phys & Life Sci Directorate Berkeley CA 94720 USA Lawrence Berkeley Natl Lab BELLA Ctr Berkeley CA 94720 USA

The generation of short pulses of ion beams through the interaction of an intense laser with a plasma sheath offers the possibility of compact and cheaper ion sources for many applications--from fast ignition and radiography of dense targets to hadron therapy and injection into conventional accelerators. To enable the efficient analysis of large-scale, high-fidelity particle accelerator simulations using the Warp simulation suite, the authors introduce the Warp In situ visualization Toolkit (WarpIV). WarpIV integrates state-of-the-art in situ visualization and analysis using VisIt with Warp, supports management and control of complex in situ visualization and analysis workflows, and implements integrated analytics to facilitate query- and feature-based data analytics and efficient large-scale data analysis. WarpIV enables for the first time distributed parallel, in situ visualization of the full simulation data using high-performance compute resources as the data is being generated by Warp. The authors describe the application of WarpIV to study and compare large 2D and 3D ion accelerator simulations, demonstrating significant differences in the acceleration process in 2D and 3D simulations. WarpIV is available to the public via https://***/berkeleylab/warpiv. The Warp In situ visualization Toolkit (WarpIV) supports large-scale, parallel, in situ visualization and analysis and facilitates query- and feature-based analytics, enabling for the first time high-performance analysis of large-scale, high-fidelity particle accelerator simulations while the data is being generated by the Warp simulation suite. This supplemental material https://***/extra/*** provides more details regarding the memory profiling and optimization and the Yee grid recentering optimization results discussed in the main article. [ABSTRACT FROM PUBLISHER]

关键词： Acceleration Analytical models Computational modeling computer graphics data analysis data models data visualization in situ visualization Ion accelerators ion beams nuclear sciences Object oriented modeling particle beams particle rendering plasma sciences

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Portable and Composable Flow Graphs for In Situ Analytics 11

Portable and Composable Flow Graphs for In Situ Analytics

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ieee 11th symposium on large data analysis and visualization (LDAV)

作者： Shudler, Sergei Petruzza, Steve Pascucci, Valerio Bremer, Peer-Timo Lawrence Livermore Natl Lab Livermore CA 94550 USA Utah State Univ Logan UT 84322 USA Univ Utah Salt Lake City UT 84112 USA

ISBN: (纸本)9781665432832

Existing data analysis and visualization algorithms are used in a wide range of simulations that strive to support an increasing number of runtime systems. The BabelFlow framework has been designed to address this situation by providing users with a simple interface to implement analysis algorithms as dataflow graphs portable across different runtimes. The limitation in BabelFlow, however, is that the graphs are not easily reusable. Plugging them into existing in situ workflows and constructing more complex graphs is difficult. In this paper, we introduce LegoFlow, an extension to BabelFlow that addresses these challenges. Specifically, we integrate LegoFlow into Ascent, a flyweight framework for large scale in situ analytics, and provide a graph composability mechanism. This mechanism is an intuitive approach to link an arbitrary number of graphs together to create more complex patterns, as well as avoid costly reimplementations for minor modifications. Without sacrificing portability, LegoFlow introduces complete flexibility that maximizes the productivity of in situ analytics workflows. Furthermore, we demonstrate a complete LULESH simulation with LegoFlow-based in situ visualization running on top of Charm++. It is a novel approach for in situ analytics, whereby the asynchronous tasking runtime allows routines for computation and analysis to overlap. Finally, we evaluate a number of LegoFlow-based filters and extracts in Ascent, as well as the scaling behavior of a LegoFlow graph for Radix-k based image compositing.

关键词： Software and its engineering data flow architectures Human-centered computing visualization Scientific visualization

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A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration 5

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exp...

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ieee 5th symposium on large data analysis and visualization (LDAV)

作者： Wong, Pak Chung Haglin, David Gillen, David Chavarria, Daniel Castellana, Vito Joslyn, Cliff Chappell, Alan Zhang, Song Pacific Northwest Natl Lab Richland WA 99352 USA Mississippi State Univ Mississippi State MS 39762 USA

ISBN: (纸本)9781467385176

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with similar to one trillion edges and similar to 50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with similar to 25 billion edges in less than two hours and 2) support database query and interactive visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.

关键词： Visual analytics graph visualization web-scale graph NetFlow triple-store RDF SPARQL high-performance computing Linux cluster

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