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Visualizing multiscale, multiphysics simulation data: Brain blood flow

Visualizing multiscale, multiphysics simulation data: Brain ...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Joseph A. Insley Leopold Grinberg Michael E. Papka Argonne National Laboratory USA Brown University USA

Accurately predicting many physical and biological systems requires modeling interactions of macroscopic and microscopic events. This results in large and heterogeneous data sets on vastly differing scales, both physical and temporal. The ability to use a single integrated tool for the visualization of multiscale simulation data is important to understanding the effects that events at one scale have on events in the other. In the case of blood flow, we examine how the large-scale flow patterns influence blood cell behavior. In this paper we describe the visualization tools that were developed for data from coupled continuum - atomistic simulations. Specifically, we overview a) a custom ParaView reader plug-in that processes macro-scale continuum data computed by a high-order spectral element solver; and b) an adaptive proper orthogonal decomposition-based technique for the visualization of nonstationary velocity fields from atomistic simulations. We also discuss how the ParaView parallel processing and rendering infrastructure was leveraged in the new tools. We apply our methods to visualize multiscale data from coupled continuum-atomistic simulations of blood flow in a patient-specific cerebrovasculature with a brain aneurysm.

关键词： data visualization Computational modeling data models Blood Brain modeling Biological system modeling Aneurysm

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Multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates

Multi-dimensional transfer function design based on flexible...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Hanqi Guo He Xiao Xiaoru Yuan Center of Computational Science and Engineering Peking University Beijing China Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing China Peking University Beijing Beijing CN

In this paper, we present an effective transfer function (TF) design for multivariate volume, providing tightly coupled views of parallel coordinates plot (PCP), MDS-based dimension projection plots, and volume rendered image space. In our design, the PCP showing the data distribution of each variate dimension and the MDS showing reduced dimensional features are integrated seamlessly to provide flexible feature classification for the user without context switching between different data presentations. Our proposed interface enables users to identify interested clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, sketching directly on the volume rendered images has been implemented to probe and edit features. To achieve interactivity, octree partitioning with Gaussian Mixture Model (GMM), and other data reduction techniques are applied. Our experiments show that the proposed method is effective for multidimensional TF design and data exploration.

关键词： Rendering (computer graphics) data visualization Octrees Algorithm design and analysis Transfer functions Strips Context

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Loose capacity-constrained representatives for the qualitative visual analysis in molecular dynamics

Loose capacity-constrained representatives for the qualitati...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Steffen Frey Thomas Schlömer Sebastian Grottel Carsten Dachsbacher Oliver Deussen Thomas Ertl Visualization Research Center Universitat Stuttgart (VISUS) Germany University of Konstanz Germany Visualization Research Center Universitat Stuttgart (VISUS) Universität Stuttgart Germany University of Konstanz Karlsruhe Institute of Technology Germany

Molecular dynamics is a widely used simulation technique to investigate material properties and structural changes under external forces. The availability of more powerful clusters and algorithms continues to increase the spatial and temporal extents of the simulation domain. This poses a particular challenge for the visualization of the underlying processes which might consist of millions of particles and thousands of time steps. Some application domains have developed special visual metaphors to only represent the relevant information of such data sets but these approaches typically require detailed domain knowledge that might not always be available or applicable. We propose a general technique that replaces the huge amount of simulated particles by a smaller set of representatives that are used for the visualization instead. The representatives capture the characteristics of the underlying particle density and exhibit coherency over time. We introduce loose capacity-constrained Voronoi diagrams for the generation of these representatives by means of a GPU-friendly, parallel algorithm. This way we achieve visualizations that reflect the particle distribution and geometric structure of the original data very faithfully. We evaluate our approach using real-world data sets from the application domains of material science, thermodynamics and dynamical systems theory.

关键词： graphics processing unit data visualization Measurement Density functional theory visualization Clustering algorithms parallel algorithms

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Evaluating the benefits of an extended memory hierarchy for parallel streamline algorithms

Evaluating the benefits of an extended memory hierarchy for ...

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ieee symposium on large data Analysis and visualization (LDAV)

作者： David Camp Hank Childs Amit Chourasia Christoph Garth Kenneth I. Joy Lawrence Berkeley National Laboratory and Department of Computer Science University of California Davis USA San Diego SupercomputerCenter University of California SanDiego USA Department of Computer Science University of California Davis USA

The increasing cost of achieving sufficient I/O bandwidth for high end supercomputers is leading to architectural evolutions in the I/O subsystem space. Currently popular designs create a staging area on each compute node for data output via solid state drives (SSDs), local hard drives, or both. In this paper, we investigate whether these extensions to the memory hierarchy, primarily intended for computer simulations that produce data, can also benefit visualization and analysis programs that consume data. Some algorithms, such as those that read the data only once and store the data in primary memory, can not draw obvious benefit from the presence of a deeper memory hierarchy. However, algorithms that read data repeatedly from disk are excellent candidates, since the repeated reads can be accelerated by caching the first read of a block on the new resources (i.e. SSDs or hard drives). We study such an algorithm, streamline computation, and quantify the benefits it can derive.

关键词： data visualization High definition video Vectors Magnetic cores Acceleration Bandwidth Toroidal magnetic fields

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Optimized GPU histograms for multi-modal registration

Optimized GPU histograms for multi-modal registration

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ieee International symposium on Biomedical Imaging

作者： Christoph Vetter Rüdiger Westermann Siemens AG Corporate Research and Development Princeton USA Computer Graphics and Visualization Technische Universität München Germany

ISBN: (纸本)9781424441273

GPU-based systems are used more and more for medical image processing because of their parallel processing power and memory bandwidth. Impressive results have been achieved when registering large volume, however, one of the most-used similarity measures for multi-modal registration - mutual information - is not well suited for the streaming architecture because of its memory access pattern. We present two optimization approaches that improve the performance by a factor of four compared to state-of-the-art GPU algorithms in the latest research papers.

关键词： Biomedical imaging Histograms Streaming media

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parallel in situ indexing for data-intensive computing

Parallel in situ indexing for data-intensive computing

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ieee symposium on large data Analysis and visualization (LDAV)

作者： Jinoh Kim Hasan Abbasi Luis Chacón Ciprian Docan Scott Klasky Qing Liu Norbert Podhorszki Arie Shoshani Kesheng Wu Lawrence Berkeley National Laboratory USA University of Tennessee Knoxville USA Oak Ridge National Laboratory USA Rutgers University USA

As computing power increases exponentially, vast amount of data is created by many scientific research activities. However, the bandwidth for storing the data to disks and reading the data from disks has been improving at a much slower pace. These two trends produce an ever-widening data access gap. Our work brings together two distinct technologies to address this data access issue: indexing and in situ processing. From decades of database research literature, we know that indexing is an effective way to address the data access issue, particularly for accessing relatively small fraction of data records. As data sets increase in sizes, more and more analysts need to use selective data access, which makes indexing an even more important for improving data access. The challenge is that most implementations of indexing technology are embedded in large database management systems (DBMS), but most scientific datasets are not managed by any DBMS. In this work, we choose to include indexes with the scientific data instead of requiring the data to be loaded into a *** use compressed bitmap indexes from the FastBit software which are known to be highly effective for query-intensive workloads common to scientific data analysis. To use the indexes, we need to build them first. The index building procedure needs to access the whole data set and may also require a significant amount of compute time. In this work, we adapt the in situ processing technology to generate the indexes, thus removing the need of reading data from disks and to build indexes in parallel. The in situ data processing system used is ADIOS, a middleware for high-performance I/O. Our experimental results show that the indexes can improve the data access time up to 200 times depending on the fraction of data selected, and using in situ data processing system can effectively reduce the time needed to create the indexes, up to 10 times with our in situ technique when using identical parallel settings.

关键词： Indexing data processing parallel processing Buildings data models Computational modeling

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VisIO: Enabling Interactive visualization of Ultra-Scale, Time Series data via High-Bandwidth Distributed I/O Systems

VisIO: Enabling Interactive Visualization of Ultra-Scale, Ti...

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Christopher Mitchell James Ahrens Jun Wang Department of Electrical Engineering & Computer Science University of Central Florida Orlando FL USA Computer Computational & Statistical Sciences Los Alamos National Laboratory Los Alamos NM USA

Petascale simulations compute at resolutions ranging into billions of cells and write terabytes of data for visualization and analysis. Interactive visualization of this time series is a desired step before starting a new run. The I/O subsystem and associated network often are a significant impediment to interactive visualization of time-varying data, as they are not configured or provisioned to provide necessary I/O read rates. In this paper, we propose a new I/O library for visualization applications: VisIO. visualization applications commonly use N-to-N reads within their parallel enabled readers which provides an incentive for a shared-nothing approach to I/O, similar to other data-intensive approaches such as Hadoop. However, unlike other data-intensive applications, visualization requires: (1) interactive performance for large data volumes, (2) compatibility with MPI and POSIX file system semantics for compatibility with existing infrastructure, and (3) use of existing file formats and their stipulated data partitioning rules. VisIO, provides a mechanism for using a non-POSIX distributed file system to provide linear scaling of I/O bandwidth. In addition, we introduce a novel scheduling algorithm that helps to co-locate visualization processes on nodes with the requested data. Testing using VisIO integrated into Para View was conducted using the Hadoop Distributed File System (HDFS) on TACC's Longhorn cluster. A representative dataset, VPIC, across 128 nodes showed a 64.4% read performance improvement compared to the provided Lustre installation. Also tested, was a dataset representing a global ocean salinity simulation that showed a 51.4% improvement in read performance over Lustre when using our VisIO system. VisIO, provides powerful high-performance I/O services to visualization applications, allowing for interactive performance with ultra-scale, time-series data.

关键词： data visualization Pipelines Distributed databases Testing Libraries File systems Computational modeling

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The large Scale data Facility: data Intensive Computing for Scientific Experiments

The Large Scale Data Facility: Data Intensive Computing for ...

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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Ariel O. Garcia Serguei Bourov Ahmad Hammad Jos van Wezel Bernhard Neumair Achim Streit Volker Hartmann Thomas Jejkal Patrick Neuberger Rainer Stotzka Steinbuch Centre of Computing Karlsruhe Institute of Technology Karlsruhe Germany Institute of Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe Germany

The large Scale data Facility (LSDF) at the Karlsruhe Institute of Technology was started end of 2009 with the aim of supporting the growing requirements of data intensive experiments. In close cooperation with the involved scientific communities, the LSDF provides them not only with adequate storage space but with a directly attached analysis farm and - more importantly - with value added services for their big scientific data-sets. Analysis workflows are supported through the mixed Hadoop and Open Nebula Cloud environments directly attached to the storage, and enable the efficient processing of the experimental data. Metadata handling is a central part of the LSDF, where a metadata repository, community specific metadata schemes, graphical tools, and APIs were developed for accessing and efficiently organizing the stored data-sets.

关键词： Software data visualization Communities Microscopy Throughput Protocols Memory

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Analysis of large digital collections with interactive visualization

Analysis of large digital collections with interactive visua...

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ieee symposium on Visual Analytics Science and Technology (VAST)

作者： Weijia Xu Maria Esteva Suyog Dutt Jain Varun Jain University of Texas Austin USA

To make decisions about the long-term preservation and access of large digital collections, archivists gather information such as the collections' contents, their organizational structure, and their file format composition. To date, the process of analyzing a collection - from data gathering to exploratory analysis and final conclusions - has largely been conducted using pen and paper methods. To help archivists analyze large-scale digital collections for archival purposes, we developed an interactive visual analytics application. The application narrows down different kinds of information about the collection, and presents them as meaningful data views. Multiple views and analysis features can be linked or unlinked on demand to enable researchers to compare and contrast different analyses, and to identify trends. We describe and present two user scenarios to show how the application allowed archivists to learn about a collection with accuracy, facilitated decision-making, and helped them arrive at conclusions.

关键词： data visualization visualization Rendering (computer graphics) data mining Image color analysis Layout Analytical models

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A Study of parallel Particle Tracing for Steady-State and Time-Varying Flow Fields

A Study of Parallel Particle Tracing for Steady-State and Ti...

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Tom Peterka Robert Ross Boonthanome Nouanesengsy Teng-Yok Lee Han-Wei Shen Wesley Kendall Jian Huang Argonne National Laboratory Argonne IL USA Ohio State Uinversity Columbus OH USA University of Tennessee Knoxville Knoxville TN USA

Particle tracing for streamline and path line generation is a common method of visualizing vector fields in scientific data, but it is difficult to parallelize efficiently because of demanding and widely varying computational and communication loads. In this paper we scale parallel particle tracing for visualizing steady and unsteady flow fields well beyond previously published results. We configure the 4D domain decomposition into spatial and temporal blocks that combine in-core and out-of-core execution in a flexible way that favors faster run time or smaller memory. We also compare static and dynamic partitioning approaches. Strong and weak scaling curves are presented for tests conducted on an IBM Blue Gene/P machine at up to 32 K processes using a parallel flow visualization library that we are developing. datasets are derived from computational fluid dynamics simulations of thermal hydraulics, liquid mixing, and combustion.

关键词： Partitioning algorithms Heuristic algorithms data structures Libraries Load management Steady-state Rendering (computer graphics)

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