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Computational reproducibility of scientific workflows at extreme scales.

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Title: Computational reproducibility of scientific workflows at extreme scales.
Authors: Pouchard, Line1 (AUTHOR) pouchard@bnl.gov, Baldwin, Sterling2 (AUTHOR), Elsethagen, Todd3 (AUTHOR), Jha, Shantenu1 (AUTHOR), Raju, Bibi3 (AUTHOR), Stephan, Eric3 (AUTHOR), Tang, Li4 (AUTHOR), Van Dam, Kerstin Kleese1 (AUTHOR), Mascagni, Michael (AUTHOR)
Source: International Journal of High Performance Computing Applications. Sep2019, Vol. 33 Issue 5, p763-776. 14p.
Subjects: Workflow management, Molecular dynamics, Key performance indicators (Management)
Abstract: We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics. We discuss two use cases: scientific reproducibility of results in the Energy Exascale Earth System Model (E3SM—previously ACME) and performance reproducibility in molecular dynamics workflows on HPC platforms. To capture and persist the provenance and performance data of these workflows, we have designed and developed the Chimbuko and ProvEn frameworks. Chimbuko captures provenance and enables detailed single workflow performance analysis. ProvEn is a hybrid, queryable system for storing and analyzing the provenance and performance metrics of multiple runs in workflow performance analysis campaigns. Workflow provenance and performance data output from Chimbuko can be visualized in a dynamic, multilevel visualization providing overview and zoom-in capabilities for areas of interest. Provenance and related performance data ingested into ProvEn is queryable and can be used to reproduce runs. Our provenance-based approach highlights challenges in extracting information and gaps in the information collected. It is agnostic to the type of provenance data it captures so that both the reproducibility of scientific results and that of performance can be explored with our tools. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 138439751
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Computational reproducibility of scientific workflows at extreme scales.
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  Data: <searchLink fieldCode="AR" term="%22Pouchard%2C+Line%22">Pouchard, Line</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pouchard@bnl.gov</i><br /><searchLink fieldCode="AR" term="%22Baldwin%2C+Sterling%22">Baldwin, Sterling</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elsethagen%2C+Todd%22">Elsethagen, Todd</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jha%2C+Shantenu%22">Jha, Shantenu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Raju%2C+Bibi%22">Raju, Bibi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stephan%2C+Eric%22">Stephan, Eric</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Li%22">Tang, Li</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Van+Dam%2C+Kerstin+Kleese%22">Van Dam, Kerstin Kleese</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mascagni%2C+Michael%22">Mascagni, Michael</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+High+Performance+Computing+Applications%22">International Journal of High Performance Computing Applications</searchLink>. Sep2019, Vol. 33 Issue 5, p763-776. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Workflow+management%22">Workflow management</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+dynamics%22">Molecular dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Key+performance+indicators+%28Management%29%22">Key performance indicators (Management)</searchLink>
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  Data: We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics. We discuss two use cases: scientific reproducibility of results in the Energy Exascale Earth System Model (E3SM—previously ACME) and performance reproducibility in molecular dynamics workflows on HPC platforms. To capture and persist the provenance and performance data of these workflows, we have designed and developed the Chimbuko and ProvEn frameworks. Chimbuko captures provenance and enables detailed single workflow performance analysis. ProvEn is a hybrid, queryable system for storing and analyzing the provenance and performance metrics of multiple runs in workflow performance analysis campaigns. Workflow provenance and performance data output from Chimbuko can be visualized in a dynamic, multilevel visualization providing overview and zoom-in capabilities for areas of interest. Provenance and related performance data ingested into ProvEn is queryable and can be used to reproduce runs. Our provenance-based approach highlights challenges in extracting information and gaps in the information collected. It is agnostic to the type of provenance data it captures so that both the reproducibility of scientific results and that of performance can be explored with our tools. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Key performance indicators (Management)
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              Text: Sep2019
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