In situ fragment detection at scale

Fabian, Nathan D.

doi:10.1109/ldav.2012.6378983

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…The refined version dramatically improves the performance. This corroborates our previous work [11], but we are now able to take the scaling out to a larger scale to see that the performance appears to be flattening out (although this might be in part due to a small number of blocks per core).…”

Section: Total Execution Timesupporting

confidence: 89%

“…With this data, our refined algorithm skips the global communication leaving only the more scalable boundary-data passing. Our analysis shows that the refined algorithm is much more scalable than the baseline algorithm [11]. Unfortunately, we cannot apply the refined algorithm in the in transit workflow because this workflow redistributes the data and invalidates this neighborhood information from CTH.…”

Section: Experiments Drivermentioning

confidence: 98%

“…Subsequent work lead to the development of Catalyst [12] and the scaling of algorithms used in conjunction with CTH [11].…”

Section: Referencesmentioning

confidence: 99%

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

Rogers¹,

Moreland²,

Oldfield³

et al. 2013

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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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Section: Total Execution Timesupporting

confidence: 89%

Section: Experiments Drivermentioning

confidence: 98%

See 1 more Smart Citation

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

Rogers¹,

Moreland²,

Oldfield³

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…With this data, our refined algorithm skips the global communication leaving only the more scalable boundary-data passing. Our analysis shows that the refined algorithm is much more scalable than the baseline algorithm [10]. Unfortunately, we cannot apply the refined algorithm in the in transit workflow because this workflow redistributes the data and invalidates this neighborhood information from CTH.…”

Section: Application Drivermentioning

confidence: 98%

“…Subsequent work lead to the development of Catalyst [11] and the scaling of algorithms used in conjunction with CTH [10].…”

Section: Catalystmentioning

confidence: 99%

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

Oldfield

Moreland

Fabian

et al. 2014

Proceedings of the 28th ACM International Conference on Supercomputing

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Exascale supercomputing will embody many revolutionary changes in the hardware and software of high-performance computing. For example, projected limitations in power and I/O-system performance will fundamentally change 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, post-processing approaches will not be able to capture the volume or granularity of data necessary for analysis of these extreme-scale simulations. As an alternative, researchers are exploring ways to integrate analysis and simulation without using the storage system. In situ and in transit are two options, but there has not been an adequate evaluation of these approaches to identify strengths, weaknesses, and trade-offs at large scale. This paper provides a detailed performance and scaling analysis of a largescale shock physics code using traditional post-processsing, in situ, and in transit analysis to detect material fragments from a simulated explosion.

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