Examples of
            <i>in transit</i>
            visualization

Moreland, Kenneth D.; Oldfield, Ron A.; Marion, Pat; Jourdain, Sébastien; Podhorszki, Norbert; Vishwanath, Venkatram; Fabian, Nathan D.; Docan, Ciprian; Parashar, Manish; Hereld, Mark; Papka, Michael E.; Klasky, Scott

doi:10.1145/2110205.2110207

Cited by 38 publications

(24 citation statements)

References 31 publications

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“…GLEAN, ADIOS, and NESSIE are just three examples for frameworks designed for in transit data processing [13]. The "loose," in transit coupling of the visualization with the simulation can improve performance and reduce I/O and storage cost by enabling processing of data without having to write the data to disk and hiding latency of the persistent data store when writing data to disk.…”

Section: In Situ Visualization and Analyticsmentioning

confidence: 99%

WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations

Rübel

Loring

Vay

et al. 2016

IEEE Comput. Grap. Appl.

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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, we 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 highperformance compute resources as the data is being generated by Warp. We 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://bitbucket.org/berkeleylab/warpiv . IEEE CG&A, SPECIAL ISSUE ON HIGH PERFORMANCE VISUALIZATION AND ANALYSIS, SEPTEMBER 2015 1In situ Visualization and Analysis of Ion Accelerator Simulations using Warp and VisIt Oliver Rübel*, Burlen Loring*, Jean-Luc Vay, David P. Grote, Remi Lehe, Stepan Bulanov, Henri Vincenti, and E .Wes Bethel Abstract-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, we 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.

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Section: In Situ Visualization and Analyticsmentioning

confidence: 99%

WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations

Rübel

Loring

Vay

et al. 2016

IEEE Comput. Grap. Appl.

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“…As a result of these trends, in situ processing has become increasingly popular with many successful usages in recent years [8,12,14,17,20]. Further, an additional advantage of in situ processing is that it can access all of the simulation data, which has never previously been possible with post hoc analysis.…”

Section: In Situmentioning

confidence: 99%

Data Exploration at the Exascale

Childs

2015

JSFI

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In situ processing -i.e., coupling visualization routines to a simulation code to generate images in real-time -is predicted to be the dominant form for visualization on upcoming supercomputers. Unfortunately, traditional in situ techniques are largely incongruent with exploratory visualization, which is an important activity to enable understanding of simulation data. In response, a new paradigm is emerging: data is transformed and massively reduced in situ and then the resulting form is explored post hoc. The fundamental tension in this approach is between the extent of the data reduction and the loss in integrity in the resulting data. However, new opportunities, in terms of increased access to data, may blunt this tension and allow for both sufficient data reduction and also more accurate analysis. With this paper, we describe the trends behind "data exploration at the exascale" and also summarize some recent results that confirmed that this new paradigm can produce superior results compared to the traditional one.

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“…For example, we developed services for staging checkpoint data [23,24,31], HPC database integration [30], interactive visualization [25], network traffic analysis, and most recently CTH in transit analysis [22]. A recent paper describes these services in detail [17].…”

Section: Nessiementioning

confidence: 99%

Data co-processing for extreme scale analysis level II ASC milestone (4745).

Rogers¹,

Moreland²,

Oldfield³

et al. 2013

Self Cite

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Exascale supercomputing will embody many revolutionary changes in the hardware and software of high-performance computing. A particularly pressing issue is gaining insight into the science behind the exascale computations. Power and I/O speed constraints will fundamentally change current visualization and analysis workflows. A traditional post-processing workflow involves storing simulation results to disk and later retrieving them for visualization and data analysis. However, at exascale, scientists and analysts will need a range of options for moving data to persistent storage, as the current offline or post-processing pipelines will not be able to capture the data necessary for data analysis of these extreme scale simulations. This Milestone explores two alternate workflows, characterized as in situ and in transit, and compares them. We find each to have its own merits and faults, and we provide information to help pick the best option for a particular use.

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Examples of in transit visualization

Cited by 38 publications

References 31 publications

WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations

WarpIV: In Situ Visualization and Analysis of Ion Accelerator Simulations

Data Exploration at the Exascale

Data co-processing for extreme scale analysis level II ASC milestone (4745).

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