Evaluation of methods to integrate analysis into a large-scale shock shock physics code

Oldfield, Ron A.; Moreland, Kenneth D.; Fabian, Nathan D.; Rogers, David

doi:10.1145/2597652.2597668

Cited by 18 publications

(6 citation statements)

References 34 publications

(28 reference statements)

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“…-Oldfield et al [17] also considered in-transit and in-line costs. The main difference between their work and our own is that they focused on analysis tasks which did not benefit from a V CEF speedup.…”

Section: Related Workmentioning

confidence: 99%

Opportunities for Cost Savings with In-Transit Visualization

Kress

Larsen

Choi

et al. 2020

Lecture Notes in Computer Science

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We analyze the opportunities for in-transit visualization to provide cost savings compared to in-line visualization. We begin by developing a cost model that includes factors related to both in-line and intransit which allows comparisons to be made between the two methods. We then run a series of studies to create a corpus of data for our model. We run two different visualization algorithms, one that is computation heavy and one that is communication heavy with concurrencies up to 32, 768 cores. Our primary results are in exploring the cost model within the context of our corpus. Our findings show that in-transit consistently achieves significant cost efficiencies by running visualization algorithms at lower concurrency, and that in many cases these efficiencies are enough to offset other costs (transfer, blocking, and additional nodes) to be cost effective overall. Finally, this work informs future studies, which can focus on choosing ideal configurations for in-transit processing that can consistently achieve cost efficiencies.

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Section: Related Workmentioning

confidence: 99%

Opportunities for Cost Savings with In-Transit Visualization

Kress

Larsen

Choi

et al. 2020

Lecture Notes in Computer Science

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“…First, simulation data is preserved in their format and not replicated in the DBMS. Second, data is related among different files while it is being generated, which might be cumbersome after the simulation ends, as in post-processing approaches [23]. Third, the history of data generation is registered for further analysis or reproduction through provenance, following W3C PROV-DM [24].…”

Section: Data Analysis Toolsmentioning

confidence: 99%

In situ visualization and data analysis for turbidity currents simulation

Camata

Silva

Valduriez

et al. 2018

Computers & Geosciences

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International audienceTurbidity currents are underflows responsible for sediment deposits that generate geological formations of interest for the oil and gas industry. libMesh-sedimentation is an application built upon the libMesh library to simulate turbidity currents. In this work, we present the integration of libMesh-sedimentation with in situ visualization and in transit data analysis tools. DfAnalyzer is a solution based on provenance data to extract and relate strategic simulation data in transit from multiple data for online queries. We integrate libMesh-sedimentation and ParaView Catalyst to perform in situ data analysis and visualization. We present a parallel performance analysis for two turbidity currents simulations showing that the overhead for both in situ visualization and in transit data analysis is negligible. We show that our tools enable monitoring the sediments appearance at runtime and steer the simulation based on the solver convergence and visual information on the sediment deposits, thus enhancing the analytical power of turbidity currents simulations

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“…Catalyst enables Hybrid workflow using VTK's I/O capabilities or by leveraging additional middleware such as Nessie [OMFR14]. For example, analysis methods and visualization pipelines could send intermediate results to burst buffers, and ParaView or another application would pull data from the burst buffers for interaction and/or further analysis.…”

Section: In Depth Analysis Of Four In Situ Infrastructuresmentioning

confidence: 99%

In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms

Bauer

Abbasi

Ahrens

et al. 2016

Computer Graphics Forum

144

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The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed/visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.

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Evaluation of methods to integrate analysis into a large-scale shock shock physics code

Cited by 18 publications

References 34 publications

Opportunities for Cost Savings with In-Transit Visualization

Opportunities for Cost Savings with In-Transit Visualization

In situ visualization and data analysis for turbidity currents simulation

In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms

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