Rogers et al 2013a - Revision history

Scipediacontent at 10:26, 22 January 2021

2021-01-22T10:26:24Z

Scipediacontent: Scipediacontent moved page Draft Content 566381858 to Rogers et al 2013a

2020-10-26T08:57:38Z

Scipediacontent moved page Draft Content 566381858 to Rogers et al 2013a

Scipediacontent: Created page with " == Abstract == Exascale supercomputing will embody many revolutionary changes in the hardware and software of high-performance computing. A particularly pressing issue is ga..."

2020-10-26T08:57:36Z

Created page with " == Abstract == Exascale supercomputing will embody many revolutionary changes in the hardware and software of high-performance computing. A particularly pressing issue is ga..."

New page

== Abstract ==

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 con- straints will fundamentally change current visualization and analysis work ows. A traditional post-processing work ow involves storing simulation results to disk and later retrieving them for visualization and data analysis. However, at exascale, scien- tists and analysts will need a range of options for moving data to persistent storage, as the current o ine 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 work ows, characterized as in situ and in transit, and compares them. We nd each to have its own merits and faults, and we provide information to help pick the best option for a particular use.

Document type: Report

== Full document ==
<pdf>Media:Draft_Content_566381858-beopen86-8591-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.2172/1093707 http://dx.doi.org/10.2172/1093707]

* [https://digital.library.unt.edu/ark:/67531/metadc844684/m2/1/high_res_d/1093707.pdf https://digital.library.unt.edu/ark:/67531/metadc844684/m2/1/high_res_d/1093707.pdf]

* [https://core.ac.uk/display/71265622 https://core.ac.uk/display/71265622],[https://academic.microsoft.com/#/detail/2465592006 https://academic.microsoft.com/#/detail/2465592006]

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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 con- straints will fundamentally change current visualization and analysis work ows. A traditional post-processing work ow involves storing simulation results to disk and later retrieving them for visualization and data analysis. However, at exascale, scien- tists and analysts will need a range of options for moving data to persistent storage, as the current o ine 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 work ows, characterized as in situ and in transit, and compares them. We nd each to have its own merits and faults, and we provide information to help pick the best option for a particular use.
@@ Line 18: / Line 13: @@
 * [https://digital.library.unt.edu/ark:/67531/metadc844684/m2/1/high_res_d/1093707.pdf https://digital.library.unt.edu/ark:/67531/metadc844684/m2/1/high_res_d/1093707.pdf]
-* [https://core.ac.uk/display/71265622 https://core.ac.uk/display/71265622],[https://academic.microsoft.com/#/detail/2465592006 https://academic.microsoft.com/#/detail/2465592006]
+* [https://core.ac.uk/display/71265622 https://core.ac.uk/display/71265622],

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