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30th ieee International parallel and Distributed Processing symposium (IPDPS)

作者： Pugmire, David Kress, James Choi, Jong Klasky, Scott Kurc, Tahsin Churchill, Randy Michael Wolf, Matthew Eisenhauer, Greg Childs, Hank Wu, Kesheng Sim, Alexander Gu, Junmin Low, Jonathan Oak Ridge Natl Lab Oak Ridge TN 37830 USA Univ Oregon Eugene OR 97403 USA SUNY Stony Brook Stony Brook NY USA Princeton Plasma Phys Lab POB 451 Princeton NJ 08543 USA Georgia Inst Technol Atlanta GA 30332 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA ASTAR Computat Resource Ctr Singapore Singapore

ISBN: (纸本)9781509036820

data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. This paper discusses initial research into visualization and analysis of distributed data workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.

关键词： data visualization

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Quantitative Exploration of large Medieval Manuscripts data for the Codicological Research 6

Quantitative Exploration of Large Medieval Manuscripts Data ...

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

作者： Chandna, Swati Rindone, Francesca Dachsbacher, Carsten Stotzka, Rainer Karlsruhe Inst Technol Inst Data Proc & Elect Karlsruhe Germany Karlsruhe Inst Technol Inst Visualizat & Data Anal Karlsruhe Germany

ISBN: (纸本)9781509056590

Quantitative exploration is gaining in importance for the analysis of the digitized medieval manuscripts. While codicologists can collect such massive amounts of heterogeneous datasets digitized in high-resolution, they still lack efficient and intuitive means to explore data and answer domain-specific research questions. A new approach is needed to enable codicologists with the quantitative exploration of large of data. This paper presents a concept of a fully integrated system to enable a quantitative exploration of various layout features and their uncertainties over a large collection of medieval manuscripts. It is composed of three main components: data handling that ingests large amounts of data into a data repository, feature extraction that extracts various layout features of the manuscripts and quantitative exploration for visual analysis. In addition to this, we introduce new visualization approaches, i.e. the superimposition plot and the manuscript montage plot in combination with the parallel coordinate plot to explore structural layout features of 170,000 manuscript pages with more than 2.5 million measurements of these layout features. Our approach supports codicologists to see the overall structure of the manuscript at a single glimpse, explore heterogeneous layout features and convey uncertainties. We demonstrate typical use-case scenarios of our collaborators in codicological research where our system has enabled them to answer domain-specific questions for analysis of the medieval manuscripts data for the first time.

关键词： medieval manuscripts visualization codicology data handling feature extraction parallel coordinate plot quantitative exploration uncertainties knowledge discovery

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Block-parallel data Analysis with DIY2 6

Block-Parallel Data Analysis with DIY2

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

作者： Morozov, Dmitriy Peterka, Tom Lawrence Berkeley Natl Lab Berkeley CA USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781509056590

DIY2 is a programming model and runtime for block-parallel analytics on distributed-memory machines. Its main abstraction is block-structured data parallelism: data are decomposed into blocks;blocks are assigned to processing elements (processes or threads);computation is described as iterations over these blocks, and communication between blocks is defined by reusable patterns. By expressing computation in this general form, the DIY2 runtime is free to optimize the movement of blocks between slow and fast memories (disk and flash vs. DRAM) and to concurrently execute blocks residing in memory with multiple threads. This enables the same program to execute in-core, out-of-core, serial, parallel, single-threaded, multithreaded, or combinations thereof. This paper describes the implementation of the main features of the DIY2 programming model and optimizations to improve performance. DIY2 is evaluated on complete analysis codes.

关键词： Computational modeling Runtime Programming Analytical models Message systems data models data analysis Runtime Computer based information systems data models Programming ANALYSIS MODELS Computational modeling data analysis Blocks memory machine

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Correlating Sub-Phenomena in Performance datainthe Frequency Domain 6

Correlating Sub-Phenomena in Performance Datainthe Frequency...

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

作者： Vierjahn, Tom Hermanns, Marc-Andre Mohr, Bernd Mueller, Matthias S. Kuhlen, Torsten W. Hentschel, Bernd Rhein Westfal TH Aachen Visual Comp Inst Aachen Germany Rhein Westfal TH Aachen Chair High Performance Comp Aachen Germany Julich Supercomp Ctr Julich Germany JARA High Performance Comp Aachen Germany

ISBN: (纸本)9781509056590

Finding and understanding correlated performance behaviour of the individual functions of massively parallel high-performance computing (HPC) applications is a time-consuming task. In this poster, we propose filtered correlation analysis for automatically locating interdependencies in call-path performance profiles. Transforming the data into the frequency domain splits a performance phenomenon into sub-phenomena to be correlated separately. We provide the mathematical framework and an overview over the visualization, and we demonstrate the effectiveness of our technique.

关键词： D.2.8 [Software Engineering]: Metrics/Measurement-Performance measures I.5.4.m [Pattern Recognition]: Applications-Signal processing I.6.9 [Simulation Modeling and visualization]: visualization

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Automated Dynamic data Redistribution

Automated Dynamic Data Redistribution

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

作者： Thomas Marrinan Joseph A. Insley Silvio Rizzi François Tessier Michael E. Papka Argonne National Laboratory Lemont IL USA Northern Illinois University DeKalb IL USA

High-performance distributed memory applications often load or receive data in a format that differs from what the application uses. One such difference arises from how the application distributes data for parallel processing. data must be redistributed from how it was laid out by the producer to how the application needs the data to be laid out amongst its processes. In this paper, we present a large-scale distributed memory library, provided to developers in an easily integrated API, for automating data redistribution in MPI enabled applications. We then present the results of two scientific computing use cases to evaluate our library. The first use case highlights how dynamic data redistribution can greatly reduce load time when reading three-dimensional medical imaging data from disk. The second use case highlights how dynamic data redistribution can facilitate in-transit analysis of computational fluid dynamics, which results in smaller data output size and faster time-to-discovery.

关键词： Libraries Distributed databases Arrays data visualization Computational modeling data models Computational fluid dynamics

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Scalable Architecture for Anomaly Detection and visualization in Power Generating Assets

Scalable Architecture for Anomaly Detection and Visualizatio...

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

作者： Paras Jain Chirag Tailor Sam Ford Liexiao Ding Michael Phillips Fang Liu Nagi Gebraeel Duen Horng Chau Georgia Institute of Technology Atlanta GA US College of Computing H. Milton Stewart School of Industrial & Systems Engineering Partnership for an Advanced Computing Environment Georgia Institute of Technology Atlanta Georgia USA

Power-generating assets (e.g., jet engines, gas turbines) are often instrumented with tens to hundreds of sensors for monitoring physical and performance degradation. Anomaly detection algorithms highlight deviations from predetermined benchmarks with the goal of detecting incipient faults. We are developing an integrated system to address three key challenges within analyzing sensor data from power-generating assets: (1) difficulty in ingesting and analyzing data from large numbers of machines; (2) prevalence of false alarms generated by anomaly detection algorithms resulting in unnecessary downtime and maintenance; and (3) lack of an integrated visualization that helps users understand and explore the flagged anomalies and relevant sensor context in the energy domain. We present preliminary results and our key findings in addressing these challenges. Our system's scalable event ingestion framework, based on OpenTSDB, ingests nearly 400,000 sensor data samples per seconds using a 30 machine cluster. To reduce false alarm rates, we leverage the False Discovery Rate (FDR) algorithm which significantly reduces the number of false alarms. Our visualization tool presents the anomalies and associated content flagged by the FDR algorithm to inform users and practitioners in their decision making process. We believe our integrated platform will help reduce maintenance costs significantly while increasing asset lifespan. We are working to extend our system on multiple fronts, such as scaling to more data and more compute nodes (70 in total).

关键词： Sensor systems Detection algorithms data visualization Gas detectors Monitoring Time series analysis

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parallel Peak Pruning for Scalable SMP Contour Tree Computation 6

Parallel Peak Pruning for Scalable SMP Contour Tree Computat...

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

作者： Carr, Hamish A. Weber, Gunther H. Sewell, Christopher M. Ahrens, James P. Univ Leeds Leeds W Yorkshire England Univ Calif Davis Lawrence Berkeley Natl Lab Davis CA 95616 USA Los Alamos Natl Lab Los Alamos NM 87544 USA

ISBN: (纸本)9781509056590

As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of O(lgnlgt) parallel steps and O(nlgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.

关键词： topological analysis contour tree merge tree data parallel algorithms

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parallel Marching Blocks: A Practical Isosurfacing Algorithm for large data on Many-Core Architectures

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COMPUTER graphics FORUM 2016年第3期35卷 211-220页

作者： Liu, Baoquan Clapworthy, Gordon J. Dong, Feng Wu, Enhua Univ Bedfordshire Luton Beds England Chinese Acad Sci SKLCS IOS Beijing Peoples R China Univ Macau Zhuhai Peoples R China

Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state-of-the-art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface-extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many-core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co-operation within thread-blocks and reducing redundant computation;ultimately, an indexed triangular mesh can be produced. Experiments have shown that the proposed algorithm is much faster (up to 10x) than state-of-the-art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64x) on the same GPU.

关键词： Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer graphics]: Picture/Image Generation&mdash Viewing Algorithms

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An integrated visualization system for interactive analysis of large, heterogeneous cosmology data 9

An integrated visualization system for interactive analysis ...

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9th ieee Pacific visualization symposium, PacificVis 2016

作者： Preston, Annie Ghods, Ramyar Xie, Jinrong Sauer, Franz Leaf, Nick Ma, Kwan-Liu Rangel, Esteban Kovacs, Eve Heitmann, Katrin Habib, Salman University of California at Davis United States Northwestern University United States Argonne National Laboratory United States

ISBN: (纸本)9781509014514

Cosmological simulations produce a multitude of data types whose large scale makes them difficult to thoroughly explore in an interactive setting. One aspect of particular interest to scientists is the evolution of groups of dark matter particles, or "halos," described by merger trees. However, in order to fully understand subtleties in the merger trees, other data types derived from the simulation must be incorporated as well. In this work, we develop a novel interactive linked-view visualization system that focuses on simultaneously exploring dark matter halos, their hierarchical evolution, corresponding particle data, and other quantitative information. We employ a parallel remote renderer and a local merger tree selection tool so that users can analyze large data sets interactively. This allows scientists to assess their simulation code, understand inconsistencies in extracted data, and intuitively understand simulation behavior on all scales. We demonstrate the effectiveness of our system through a set of case studies on large-scale cosmological data from the HACC (Hardware/Hybrid Accelerated Cosmology Code) simulation framework. © 2016 ieee.

关键词： Cosmology

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Eurographics Workshop on parallel graphics and visualization

Eurographics Workshop on Parallel Graphics and Visualization

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2016 Eurographics symposium on parallel graphics and visualization, EGPGV 2016

ISBN: (纸本)9783038680062

The proceedings contain 10 papers. The topics discussed include: adaptive collision culling for large-scale simulations by a parallel sweep and prune algorithm;external face-list calculation with data-parallel primitives;parallel spatial splits in bounding volume hierarchies;interacting with large distributed datasets using sketch;high-performance mesh partitioning and ghost cell generation for visualization software;web-enabled server-based and distributed real-time ray-tracing;a scalable streamline generation algorithm via flux-based isocontour extraction;dynamically scheduled region-based image compositing;dynamic work packages in parallel rendering;and data mining tornado-genesis precursors.

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