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Time dependent processing in a parallel pipeline architecture

ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2007年第6期13卷 1376-1383页

作者： Biddiscombe, John Geveci, Berk Martin, Ken Moreland, Kenneth Thompson, David Sandia Natl Labs Livermore CA 94550 USA

Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed-memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing;plotting over time;or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.

关键词： data-parallel visualization pipeline time-varying data

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ADR visualization: A Generalized Framework for Ranking large-Scale Scientific data using Analysis-Driven Refinement 4

ADR Visualization: A Generalized Framework for Ranking Large...

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ieee symposium on large data Analysis and visualization

作者： Nouanesengsy, Boonthanome Woodring, Jonathan Patchett, John Myers, Kary Ahrens, James Los Alamos Natl Lab Los Alamos NM 87544 USA

ISBN: (纸本)9781479952151

Prioritization of data is necessary for managing large-scale scientific data, as the scale of the data implies that there are only enough resources available to process a limited subset of the data. For example, data prioritization is used during in situ triage to scale with bandwidth bottlenecks, and used during focus+context visualization to save time during analysis by guiding the user to important information. In this paper, we present ADR visualization, a generalized analysis framework for ranking large-scale data using Analysis -Driven Refinement (ADR), which is inspired by Adaptive Mesh Refinement (AMR). A large-scale data set is partitioned in space, time, and variable, using user-defined importance measurements for prioritization. This process creates a prioritization tree over the data set. Using this tree, selection methods can generate sparse data products for analysis, such as focus+context visualizations or sparse data sets.

关键词： data triage focus plus context large-scale data big data scientific data prioritization adaptive mesh refinement I.3.1 [Computer graphics]: Hardware Architecture-parallel processing I.3.3 [Computer graphics]: Picture/Image Generation-Viewing algorithms

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Visual Analysis of Cloud Computing Performance Using Behavioral Lines

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2016年第6期22卷 1694-1704页

作者： Muelder, Chris Zhu, Biao Chen, Wei Zhang, Hongxin Ma, Kwan-Liu Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Zhejiang Peoples R China

Cloud computing is an essential technology to Big data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

关键词： Cloud computing multidimensional data performance visualization visual analytics

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Distributed shared memory for roaming large volumes

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2006年第5期12卷 1299-1306页

作者： Castanie, Laurent Mion, Christophe Cavin, Xavier Levy, Bruno INRIA Earth Desis Nancy France INRIA ALICE Grp Nancy France

We present a cluster-based volume rendering system for roaming very large volumes. This system allows to move a gigabyte-sized probe inside a total volume of several tens or hundreds of gigabytes in real-time. While the size of the probe is limited by the total amount of texture memory on the cluster, the size of the total data set has no theoretical limit. The cluster is used as a distributed graphics processing unit that both aggregates graphics power and graphics memory. A hardware-accelerated volume renderer runs in parallel on the cluster nodes and the final image compositing is implemented using a pipelined sort-last rendering algorithm. Meanwhile, volume bricking and volume paging allow efficient data caching. On each rendering node, a distributed hierarchical cache system implements a global software-based distributed shared memory on the cluster. In case of a cache miss, this system first checks page residency on the other cluster nodes instead of directly accessing local disks. Using two Gigabit Ethernet network interfaces per node, we accelerate data fetching by a factor of 4 compared to directly accessing local disks. The system also implements asynchronous disk access and texture loading, which makes it possible to overlap data loading, volume slicing and rendering for optimal volume roaming.

关键词： large volumes volume roaming out-of-core hierarchical caching distributed shared memory hardware-accelerated volume visualization graphics hardware parallel rendering graphics cluster

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Case study:: parallel Lagrangian visualization applied to natural convective flows

Case study:: Parallel Lagrangian visualization applied to na...

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ieee symposium on parallel and large-data visualization and graphics

作者： de la Cruz, LM García, I Godoy, V Ramos, E Univ Nacl Autonoma Mexico Direcc Compp Invest DGSCA Mexico City 04510 DF Mexico

ISBN: (纸本)0780372239

This paper presents a visualization technique based on particle tracking. The technique consists in defining a set of points distributed on a closed surface and follow the surface deformations as the velocity field changes in time. Deformations of the surface contains information about dynamics of the flow;in particular, it is possible to identify zones where flow stretching and foldings occur. Because the points on the surface are independent each other, it is possible to calculate the trajectory of each point concurrently. Two parallel algorithms are studied;the first one for a shared memory Origin 2000 supercomputer and the second one for a distributed memory PC cluster. The technique is applied to a fluid moving by natural convection inside a cubic container.

关键词： Flow visualization particle traces pathlines shared and distributed memory

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A multilayered image cache for scientific visualization 03

A multilayered image cache for scientific visualization

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ieee symposium on parallel and large-data visualization and graphics 2003, PVG 2003

作者： LaMar, E. Pascucci, V. Lawrence Livermore Nat. Lab. BerkeleyCA United States

ISBN: (纸本)078038122X

We introduce a multilayered image cache system that is designed to work with a pool of rendering engines to facilitate a frame-less, asynchronous rendering environment for scientific visualization. Our system decouples the rendering from the display of imagery at many levels;it decouples render frequency and resolution from display frequency and resolution;allows asynchronous transmission of imagery instead of the compute-send cycle of standard parallel systems;and allows local, incremental refinement of imagery without requiring all imagery to be rerendered. Interactivity is accomplished by maintaining a set of image tiles for display while the production of imagery is performed by a pool of processors. The image tiles are placed in fixed places in camera (vs. world) space to eliminate occlusion artifacts. Display quality is improved by increasing the number of image tiles and imagery is refreshed more frequently by decreasing the number of image tiles. © 2003 ieee.

关键词： visualization

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Indexed-Points parallel Coordinates visualization of Multivariate Correlations

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2018年第6期24卷 1997-2010页

作者： Zhou, Liang Weiskopf, Daniel Univ Stuttgart Visualizat Res Ctr VISUS Allmandring 19 D-70567 Stuttgart Germany

We address the problem of visualizing multivariate correlations in parallel coordinates. We focus on multivariate correlation in the form of linear relationships between multiple variables. Traditional parallel coordinates are well prepared to show negative correlations between two attributes by distinct visual patterns. However, it is difficult to recognize positive correlations in parallel coordinates. Furthermore, there is no support to high light multivariate correlations in parallel coordinates. In this paper, we exploit the indexed point representation of p-flats (planes in multidimensional data) to visualize local multivariate correlations in parallel coordinates. Our method yields clear visual signatures for negative and positive correlations alike, and it supports large datasets. All information is shown in a unified parallel coordinates framework, which leads to easy and familiar user interactions for analysts who have experience with traditional parallel coordinates. The usefulness of our method is demonstrated through examples of typical multidimensional datasets.

关键词： Multidimensional data visualization multivariate correlations parallel coordinates

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A DECLARATIVE APPROACH TO VISUALIZING CONCURRENT COMPUTATIONS

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COMPUTER 1989年第10期22卷 25-36页

作者： ROMAN, GC COX, KC Dept. of Comput. Sci. Washington Univ. St. Louis MO

The authors explore the graphical representation of objects and processes as a means for understanding programs consisting of large numbers of concurrent processes. Their goal is to establish a technical foundation for research into the monitoring and debugging of large-scale concurrent programs. They explain declarative visualization and introduce some shared-data-space concepts and notation by means of a simple, nondeterministic, parallel algorithm. They discuss visual abstraction and their visualization methodology, which they illustrate by applying it to region labeling. Thy examine the relaxation of nonintervention that visualization makes possible

关键词： computer graphics concurrent computations data structures debugging declarative approach declarative visualization graphical representation large-scale concurrent programs monitoring nonintervention objects parallel algorithm parallel programming processes program debugging region labeling shared-data-space visual abstraction

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Adaptively Placed Multi-Grid Scene Representation Networks for large-Scale data visualization

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2024年第1期30卷 965-974页

作者： Wurster, Skylar W. Xiong, Tianyu Shen, Han-Wei Guo, Hanqi Peterka, Tom Ohio State Univ Columbus OH 43210 USA

Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems. We also release an open-source neural volume rendering application that allows plug-and-play rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses multiple spatially adaptive feature grids that learn where to be placed within the domain to dynamically allocate more neural network resources where error is high in the volume, improving state-of-the-art reconstruction accuracy of SRNs for scientific data without requiring expensive octree refining, pruning, and traversal like previous adaptive models. In our domain decomposition approach for representing large-scale data, we train an set of APMGSRNs in parallel on separate bricks of the volume to reduce training time while avoiding overhead necessary for an out-of-core solution for volumes too large to fit in GPU memory. After training, the lightweight SRNs are used for realtime neural volume rendering in our open-source renderer, where arbitrary view angles and transfer functions can be explored.

关键词： Training Adaptation models data models Solid modeling Computer architecture Rendering (computer graphics) Encoding Scene representation network deep learning scientific visualization volume rendering

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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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