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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Min-Yu Huang Gunther H. Weber Xiao-Yong Li Mark D. Biggin Bernd Hamann Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California슠Davis Davis CA USA Visualization Group Lawrence Berkeley National Laboratory Berkeley CA USA Genomics Divisions Lawrence Berkeley National Laboratory Berkeley CA USA Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California Davis Davis CA USA

Most analyses of ChIP-chip in vivo DNA binding have focused on qualitative descriptions of whether genomic regions are bound or not. There is increasing evidence, however, that factors bind in a highly overlapping manner to the same genomic regions and that it is quantitative differences in occupancy on these commonly bound regions that are the critical determinants of the different biological specificity of factors. As a result, it is critical to have a tool to facilitate the quantitative visualization of differences between transcription factors and the genomic regions they bind to understand each factor's unique roles in the network. We have developed a framework which combines several visualizations via brushing-and-linking to allow the user to interactively analyze and explore in vivo DNA binding data of multiple transcription factors. We describe these visualization types and also provide a discussion of biological examples in this paper.

关键词： Correlation Bioinformatics data visualization Genomics Image color analysis Browsers

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Fast ultrasound beam prediction for linear and regular two- dimensionalarrays

Fast ultrasound beam prediction for linear and regular two- ...

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IEEE International Ultrasonics Symposium

作者： Hlawitschka, Mario McGough, Robert J. Ferrara, Katherine W. Kruse, Dustin E. Institute for Data Analysis and Visualization University of California Davis CA 95616 United States Department of Electrical and Computer Engineering Michigan State University East Lansing MI 48824 United States Department of Biomedical Engineering University of California Davis CA 95616 United States

ISBN: (纸本)9781457703829

Real-time beam predictions are desirable for ultrasound therapy guidance,as treatment planning requires individualized computations. To address thelong-standing issue of the computational burden associated with calculating theacoustic field in large volumes, we use modern graphics processing units toaccelerate the calculation of near-field pressure fields for therapeuticultrasound arrays. First, we accelerate field computations for singlerectangular pistons by employing the Fast Near-field Method (FNM) to accuratelyand efficiently estimate the complex near-field wave patterns in homogeneousmedia, and results are compared to the Rayleigh-Sommerfeld method. We thencompute beam patterns of 1D and 2D piston arrays using pre-calculated beampatterns from a single piston. Our results show that these algorithms canbenefit greatly from GPU computing hardware, exceeding a modern CPU by a factorof over 100. For a single rectangular piston, the FNM is able to calculate a0.01% accurate field within 30 ns per field point. Furthermore, we demonstratecalculation speeds for arrays of up to 11.5 109 field points persecond. Beam volumes containing 2563 field points are calculatedwithin one second for arrays containing up to 512 pistons, thus, facilitatingfuture real-time thermal dose predictions. © 2010 IEEE.

关键词： Pistons

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Fast ultrasound beam prediction for linear and regular two-dimensional arrays

Fast ultrasound beam prediction for linear and regular two-d...

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ISBN: (纸本)9781457703829

Real-time beam predictions are desirable for ultrasound therapy guidance, as treatment planning requires individualized computations. To address the long-standing issue of the computational burden associated with calculating the acoustic field in large volumes, we use modern graphics processing units to accelerate the calculation of near-field pressure fields for therapeutic ultrasound arrays. First, we accelerate field computations for single rectangular pistons by employing the Fast Near-field Method (FNM) to accurately and efficiently estimate the complex near-field wave patterns in homogeneous media, and results are compared to the Rayleigh-Sommerfeld method. We then compute beam patterns of 1D and 2D piston arrays using pre-calculated beam patterns from a single piston. Our results show that these algorithms can benefit greatly from GPU computing hardware, exceeding a modern CPU by a factor of over 100. For a single rectangular piston, the FNM is able to calculate a 0.01% accurate field within 30 ns per field point. Furthermore, we demonstrate calculation speeds for arrays of up to 11.5 109 field points per second. Beam volumes containing 2563 field points are calculated within one second for arrays containing up to 512 pistons, thus, facilitating future real-time thermal dose predictions. © 2010 IEEE.

关键词： Pistons

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Large data visualization on Distributed Memory Multi-GPU Clusters 10

Large Data Visualization on Distributed Memory Multi-GPU Clu...

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Conference on High-performance Graphics

作者： Thomas Fogal Hank Childs Siddharth Shankar Jens Kriiger R. Daniel Bergeron Philip Hatcher Scientific Computing & Imaging Institute University of UtahDepartment of Computer Science University of New Hampshire Lawrence Berkeley National Laboratory Scientific Computing & Imaging Institute University of Utah Interactive Visualization and Data Analysis Group DFKI Germany Department of Computer Science University of New Hampshire

ISBN: (纸本)9781617827242

data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets.

关键词： distributed memory data visualisation visualization dataset supercomputer Representation of data performance characteristics

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Shrinking large visual vocabularies using multi-label agglomerative information bottleneck

Shrinking large visual vocabularies using multi-label agglom...

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IEEE International Conference on Image Processing

作者： Wojciech Wojcikiewicz Alexander Binder Motoaki Kawanabe Department of Computer Science Technical University Berlin Berlin Germany Intelligent Data Analysis Group Fraunhofer Institute FIRST Berlin Germany

The quality of visual vocabularies is crucial for the performance of bag-of-words image classification methods. Several approaches have been developed for codebook construction, the most popular method is to cluster a set of image features (e.g. SIFT) by k-means. In this paper, we propose a two-step procedure which incorporates label information into the clustering process by efficiently generating a large and informative vocabulary using class-wise k-means and reducing its size by agglomerative information bottleneck (AIB). We introduce an extension of the AIB procedure for multi-label problems and show that this two-step approach improves the classification results while reducing computation time compared to the vanilla k-means. We analyse the reasons for the performance gain on the PASCAL VOC 2007 data set.

关键词： Vocabulary visualization Kernel Performance gain Training Clustering algorithms Histograms

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Out-of-core data management for path tracing on hybrid resources

Out-of-core data management for path tracing on hybrid resou...

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30th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2009

作者： Budge, Brian Bernardin, Tony Stuart, Jeff A. Sengupta, Shubhabrata Joy, Kenneth I. Owens, John D. Institute for Data Analysis and Visualization Department of Computer Science Davis United States Department of Electrical and Computer Engineering University of California Davis United States

We present a software system that enables path-traced rendering of complex scenes. The system consists of two primary components: an application layer that implements the basic rendering algorithm, and an out-of-core scheduling and data-management layer designed to assist the application layer in exploiting hybrid computational resources (e.g., CPUs and GPUs) simultaneously. We describe the basic system architecture, discuss design decisions of the system's data-management layer, and outline an efficient implementation of a path tracer application, where GPUs perform functions such as ray tracing, shadow tracing, importance-driven light sampling, and surface shading. The use of GPUs speeds up the runtime of these components by factors ranging from two to twenty, resulting in a substantial overall increase in rendering speed. The path tracer scales well with respect to CPUs, GPUs and memory per node as well as scaling with the number of nodes. The result is a system that can render large complex scenes with strong performance and scalability. © 2008 The Author(s).

关键词： Information management

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Maximizing adaptivity in hierarchical topological models using cancellation trees

Maximizing adaptivity in hierarchical topological models usi...

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Workshop on Mathematical Foundations of Scientific visualization, computer Graphics, and Massive data Exploration, 2004

作者： Bremer, Peer-Timo Pascucci, Valerio Hamann, Bernd Department of Computer Science University of Illinois Urbana-Champaign United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Institute for Data Analysis and Visualization Department of Computer Science University of California Davis United States

ISBN: (纸本)9783642543005

We present a highly adaptive hierarchical representation of the topology of functions defined over two-manifold domains. Guided by the theory of Morse–Smale complexes, we encode dependencies between cancellations of critical points using two independent structures: a traditional mesh hierarchy to store connectivity information and a new structure called cancellation trees to encode the configuration of critical points. Cancellation trees provide a powerful method to increase adaptivity while using a simple, easy-to-implement data structure. The resulting hierarchy is significantly more flexible than the one previously reported (IEEE Trans. Vis. Comput. Graph. 10(4):385–396, 2004). In particular, the resulting hierarchy is guaranteed to be of logarithmic height. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Trees (mathematics)

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Cataclysmic Variables and Other Compact Binaries in the Globular Cluster NGC 362: Candidates from Chandra and HST

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AIP Conference Proceedings 2010年第1期1314卷 163-168页

作者： B. Margon B. Beck‐Winchatz L. Homer D. Pooley C. G. Bassa S. F. Anderson W. H. G. Lewin F. Verbunt A. K. H. Kong R. M. Plotkin aDepartment of Astronomy & Astrophysics University of California Santa Cruz CA 95064 USA bScientific Data Analysis and Visualization Program DePaul University Chicago IL 60614 USA cDepartment of Astronomy University of Washington Box 351580 Seattle WA 98195 USA dEureka Scientific 5248 Valley View Road El Sobrante CA 94803 USA eJodrell Bank Centre for Astrophysics School of Physics and Astronomy University of Manchester Manchester M13 9PL UK fKavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge MA 02139 USA gAstronomical Institute Utrecht University PO Box 80000 3508 TA Utrecht The Netherlands hInstitute of Astronomy and Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan iAstronomical Institute “Anton Pannekoek ” University of Amsterdam Science Park 904 1098 XH Amsterdam The Netherlands

Highly sensitive and precise X‐ray imaging from Chandra, combined with the superb spatial resolution of HST optical images, dramatically enhances our empirical understanding of compact binaries such as cataclysmic variables and low mass X‐ray binaries, their progeny, and other stellar X‐ray source populations deep into the cores of globular clusters. Our Chandra X‐ray images of the globular cluster NGC 362 reveal 100 X‐ray sources, the bulk of which are likely cluster members. Using HST color‐magnitude and color‐color diagrams, we quantitatively consider the optical content of the NGC 362 Chandra X‐ray error circles, especially to assess and identify the compact binary population in this condensed‐core globular cluster. Despite residual significant crowding in both X‐rays and optical, we identify an excess population of Hα‐emitting objects that is statistically associated with the Chandra X‐ray sources. The X‐ray and optical characteristics suggest that these are mainly cataclysmic variables, but we also identify a candidate quiescent low mass X‐ray binary. A potentially interesting and largely unanticipated use of observations such as these may be to help constrain the macroscopic dynamic state of globular clusters.

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Compression and occlusion culling for fast isosurface extraction from massive datasets

Compression and occlusion culling for fast isosurface extrac...

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Workshop on Mathematical Foundations of Scientific visualization, computer Graphics, and Massive data Exploration, 2004

作者： Gregorski, Benjamin Senecal, Joshua Duchaineau, Mark Joy, Kenneth I. Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue LivermoreCA94551 United States Institute for Data Analysis and Visualization Department of Computer Science University of California Davis One Shields Avenue DavisCA95616 United States

ISBN: (纸本)9783642543005

We present two algorithms for data compression and occlusion culling that improve interactive, adaptive isosurface extraction from large volume datasets. Our algorithm, based on hierarchical tetrahedral meshes defined by longest edge bisection, allows arbitrary isosurfaces to be adaptively extracted at interactive rates from losslessly compressed volumes where the region of interest, determined at runtime by user interaction, is decompressed on-the-fly. For interactive applications, we exploit frame-to-frame coherence between consecutive views to simplify the mesh structure in occluded regions and eliminate occluded triangles significantly reducing the complexity of the visualized surface and the underlying multiresolution volume representation. We extend the use of hardware accelerated occlusion queries to adaptive isosurface extraction applications where the surface geometry and topology change with the level-of-detail and view-point and the user can select an arbitrary isovalue for visualization. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Extraction

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Automatic beam path analysis of laser wakefield particle acceleration data

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Computational science and Discovery 2009年第1期2卷

作者： Rübel, Oliver Geddes, Cameron G R. Cormier-Michel, Estelle Wu, Kesheng Prabhat Weber, Gunther H. Ushizima, Daniela M. Messmer, Peter Hagen, Hans Hamann, Bernd Bethel, Wes Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 One Cyclotron Road United States 'Visualization of Large and Unstructured Data Sets - Applic. in Geospatial Plan. Modeling and Eng. 'Technische Universität Kaiserslautern D-67653 Kaiserslautern Erwin-Schrödinger-Straße Germany Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California Davis CA 95616 Davis One Shields Avenue United States LOASIS Program of Lawrence Berkeley National Laboratory Berkeley CA 94720 One Cyclotron Road United States Tech-X Corporation Boulder CO 80303 5621 Arapahoe Avenue United States

Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high-energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information-derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than has been possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline. © 2009 IOP Publishing Ltd.

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