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1st ieee symposium on large-Scale data Analysis and visualization 2011, LDAV 2011

作者： Camp, David Childs, Hank Chourasia, Amit Garth, Christoph Joy, Kenneth I. Lawrence Berkeley National Laboratory Department of Computer Science University of California Davis CA United States San Diego Supercomputer Center University of California San Diego CA United States Department of Computer Science University of California Davis CA United States

ISBN: (纸本)9781467301541

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. © 2011 ieee.

关键词： Supercomputers

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Exploring the Spectrum of Dynamic Scheduling Algorithms for Scalable Distributed-Memory Ray Tracing

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2014年第6期20卷 893-906页

作者： Navratil, Paul A. Childs, Hank Fussell, Donald S. Lin, Calvin Univ Texas Austin Texas Adv Comp Ctr Austin TX 78758 USA Univ Oregon Dept Comp & Informat Sci Eugene OR 97403 USA Lawrence Berkeley Natl Lab Berkeley CA USA Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

This paper extends and evaluates a family of dynamic ray scheduling algorithms that can be performed in-situ on large distributed memory parallel computers. The key idea is to consider both ray state and data accesses when scheduling ray computations. We compare three instances of this family of algorithms against two traditional statically scheduled schemes. We show that our dynamic scheduling approach can render data sets that are larger than aggregate system memory and that cannot be rendered by existing statically scheduled ray tracers. For smaller problems that fit in aggregate memory but are larger than typical shared memory, our dynamic approach is competitive with the best static scheduling algorithm.

关键词： Distributed memory dynamic scheduling parallel ray tracing

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Eurographics symposium on parallel graphics and visualization, EGPGV 2009

Eurographics Symposium on Parallel Graphics and Visualizatio...

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9th Eurographics symposium on parallel graphics and visualization, EGPGV 2009

ISBN: (纸本)9783905674156

The proceedings contain 14 papers. The topics discussed include: time-constrained high-fidelity rendering on local desktop grids;interactive physical simulation on multicore architectures;dynamic grid refinement for fluid simulations on parallel graphics architectures;simulation of radio wave propagation by beam tracing;parallelized matrix factorization for fast BTF compression;parallelized matrix factorization for fast BTF compression;fast parallel unbiased diffeomorphic atlas construction on multi-graphics processing units;a flexible adaptation service for distributed rendering;wait-free shared-memory irradiance cache;data-parallel hierarchical link creation for radiosity;and a decomposition approach for optimizing large-scale parallel image composition on multi-core MPP systems.

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AID*: A Spatial Index for Visual Exploration of Geo-Spatial data

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2022年第8期34卷 3569-3582页

作者： Ghosh, Saheli Eldawy, Ahmed Univ Calif Riverside Riverside CA 92521 USA

Visual exploration has become an integral part of big spatial data management. With the increase in volume and number of spatial datasets, several specialized mechanisms have been proposed to speed up the exploration of these datasets. However, the existing techniques have major limitations which make them incapable of providing visual exploration for hundreds of thousands of big datasets on a single machine. This paper introduces a new index structure, termed AID*, that facilitates the visual exploration of an arbitrarily large number of big spatial datasets on a single machine. The AID* index defines multi-resolution fixed-size tiles on the input and classifies them as image, data, shallow, or empty tiles, based on their processing cost. Then, it uses this classification to build an index with a minimal index size and construction time, while supporting the desired real-time exploration interface. The index is constructed in parallel, using Hadoop or Spark, and is accessible to end users through a standard web interface similar to Google Maps. The small size of the index allows a single-machine server to host arbitrarily many datasets. Our experiments, on up-to 1 TB of data and 27 billion records, show that the construction of the proposed index is up-to an order of magnitude faster than the baselines without compromising the end-user interactivity.

关键词： Indexes visualization Spatial databases data visualization graphics processing units Sparks Geospatial analysis Interactive exploration big spatial data indexing

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Fast Approximation of Persistence Diagrams with Guarantees 11

Fast Approximation of Persistence Diagrams with Guarantees

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

作者： Vidal, Jules Tierny, Julien Sorbonne Univ CNRS Paris France

ISBN: (纸本)9781665432832

This paper presents an algorithm for the efficient approximation of the saddle-extremum persistence diagram of a scalar field. Vidal et al. introduced recently a fast algorithm for such an approximation (by interrupting a progressive computation framework [78]). However, no theoretical guarantee was provided regarding its approximation quality. In this work, we revisit the progressive framework of Vidal et al. [78] and we introduce in contrast a novel approximation algorithm, with a user controlled approximation error, specifically, on the Bottleneck distance to the exact solution. Our approach is based on a hierarchical representation of the input data, and relies on local simplifications of the scalar field to accelerate the computation, while maintaining a controlled bound on the output error. The locality of our approach enables further speedups thanks to shared memory parallelism. Experiments conducted on real life datasets show that for a mild error tolerance (5% relative Bottleneck distance), our approach improves runtime performance by 18% on average (and up to 48% on large, noisy datasets) in comparison to standard, exact, publicly available implementations. In addition to the strong guarantees on its approximation error, we show that our algorithm also provides in practice outputs which are on average 5 times more accurate (in terms of the L-2-Wasserstein distance) than a naive approximation baseline. We illustrate the utility of our approach for interactive data exploration and we document visualization strategies for conveying the uncertainty related to our approximations.

关键词： data analysis Uncertainty Runtime data visualization parallel processing Approximation algorithms Approximation error

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In situ generated probability distribution functions for interactive post hoc visualization and analysis 6

In situ generated probability distribution functions for int...

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6th ieee symposium on large-Scale data Analysis and visualization, LDAV 2016

作者： Ye, Yucong Chris Neuroth, Tyson Sauer, Franz Ma, Kwan-Liu Borghesi, Giulio Konduri, Aditya Kolla, Hemanth Chen, Jacqueline University of California DavisCA United States Sandia National Laboratory LivermoreCA United States

ISBN: (纸本)9781509056590

The growing power and capacity of supercomputers enable scientific simulations at extreme scale, leading to not only more accurate modeling and greater predictive ability but also massive quantities of data to analyze. New approaches to data analysis and visualization are this needed to support interactive exploration through selective data access for gaining insights into terabytes and petabytes of data. In this paper, we present an in situ data processing method for both generating probability distribution functions (PDFs) from field data and reorganizing particle data using a single spatial organization scheme. This coupling between PDFs and particles allows for the interactive post hoc exploration of both data types simultaneously. Scientists can explore trends in large-scale data through the PDFs and subsequently extract desired particle subsets for further analysis. We evaluate the usability of our in situ method using a petascale combustion simulation and demonstrate the increases in task efficiency and accuracy that the resulting workflow provides to scientists. © 2016 ieee.

关键词： Distribution functions

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large-scale multiple sequence alignment visualization through gradient vector flow analysis

Large-scale multiple sequence alignment visualization throug...

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3rd ieee symposium on Biological data visualization, BioVis 2013

作者： Nguyen, Khoa Tan Ropinski, Timo Scientific Visualization Group Linköping University Sweden

ISBN: (纸本)9781479916580

Multiple sequence alignment (MSA) is essential as an initial step in studying molecular phylogeny as well as during the identification of genomic rearrangements. Recent advances in sequencing techniques have led to a tremendous increase in the number of sequences to be analyzed. As a result, a greater demand is being placed on visualization techniques, as they have the potential to reveal the underlying information in large-scale MSAs. In this work, we present a novel visualization technique for conveying the patterns in large-scale MSAs. By applying gradient vector flow analysis to the MSA data, we can extract and visually emphasize conservations and other patterns that are relevant during the MSA exploration process. In contrast to the traditional visual representation of MSAs, which exploits color-coded tables, the proposed visual metaphor allows us to provide an overview of large MSAs as well as to highlight global patterns, outliers, and data distributions. We will motivate and describe the proposed algorithm, and further demonstrate its application to large-scale MSAs. © 2013 ieee.

关键词： Conveying

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GPU Optimization for High-Quality Kinetic Fluid Simulation

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2022年第9期28卷 3235-3251页

作者： Chen, Yixin Li, Wei Fan, Rui Liu, Xiaopei ShanghaiTech Univ Shanghai Engn Res Ctr Intelligent Vis & Imaging Sch Informat Sci & Technol Shanghai 201210 Peoples R China

Fluid simulations are often performed using the incompressible Navier-Stokes equations (INSE), leading to sparse linear systems which are difficult to solve efficiently in parallel. Recently, kinetic methods based on the adaptive-central-moment multiple-relaxation-time (ACM-MRT) model [1], [2] have demonstrated impressive capabilities to simulate both laminar and turbulent flows, with quality matching or surpassing that of state-of-the-art INSE solvers. Furthermore, due to its local formulation, this method presents the opportunity for highly scalable implementations on parallel systems such as GPUs. However, an efficient ACM-MRT-based kinetic solver needs to overcome a number of computational challenges, especially when dealing with complex solids inside the fluid domain. In this article, we present multiple novel GPU optimization techniques to efficiently implement high-quality ACM-MRT-based kinetic fluid simulations in domains containing complex solids. Our techniques include a new communication-efficient data layout, a load-balanced immersed-boundary method, a multi-kernel launch method using a simplified formulation of ACM-MRT calculations to enable greater parallelism, and the integration of these techniques into a parametric cost model to enable automated prameter search to achieve optimal execution performance. We also extended our method to multi-GPU systems to enable large-scale simulations. To demonstrate the state-of-the-art performance and high visual quality of our solver, we present extensive experimental results and comparisons to other solvers.

关键词： Kinetic theory Mathematical model graphics processing units Computational modeling Solids Optimization Adaptation models GPU optimization parallel computing fluid simulation lattice Boltzmann method immersed boundary method

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SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional data Spaces 6

<i>SeekAView</i>: An Intelligent Dimensionality Reduction St...

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6th ieee symposium on large data Analysis and visualization (LDAV)

作者： Krause, Josua Dasgupta, Aritra Fekete, Jean-Daniel Bertini, Enrico NYU New York NY 10003 USA Pacific Northwest Natl Lab Richland WA 99352 USA INRIA Le Chesnay France

ISBN: (纸本)9781509056590

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.

关键词： High-Dimensional data Subspace Exploration Guided visualization

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3D Approach to the visualization of parallel Applications and Grid Monitoring Information 08

3D Approach to the Visualization of Parallel Applications an...

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9th ieee/ACM International Conference on Grid Computing

作者： Schnorr, Lucas Mello Huard, Guillaume Alexandre Navaux, Philippe Olivier Univ Fed Rio Grande do Sul Inst Informat BR-91501970 Porto Alegre RS Brazil

ISBN: (纸本)9781424425785

parallel computing is increasingly used to provide more performance to applications that need tremendous computational power. The main characteristics of distributed parallel machines are heterogeneity, dynamism and size. They influence directly the way the application and platform monitoring tasks are performed, especially when analyzing a large quantity of information collected in a topologically complex machine. This paper describes our efforts to provide parallel programmers and Grid users a new way to visualize monitoring data. Using graphics in three dimensions and information visualization techniques, we aim at bringing rich topological information to the rendered scene. It results in an immersible and human readable representation of complex monitoring data, suited to Grid environments. We first review known techniques in information visualization context, especially those that address the case of hierarchical information, and we discuss about their use in our context. Then, we propose a new 3D approach that combines the classical space-time visualization of application traces with the representation of the application's communication pattern. Finally, we present experimental results obtained through the visualization of parallel applications in our prototype.

关键词： visualization

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