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Title: Computational Reproducibility of Scientific Workflows at Extreme Scales

Abstract

We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: scientific reproducibility of results in the ACME climate simulations and performance reproducibility in molecular dynamics workflows on HPC computing platforms.

Authors:
 [1];  [2];  [3];  [4]; ORCiD logo [3]; ORCiD logo [3];  [5];  [4]
  1. Oark Ridge National Laboratory
  2. LAWRENCE LIVERMORE
  3. BATTELLE (PACIFIC NW LAB)
  4. Brookhaven National Laboratory
  5. Los Alamos National Laboratory
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1577046
Report Number(s):
PNNL-SA-137882
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
International Journal of High Performance Computing Applications
Additional Journal Information:
Journal Volume: 33; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
provenance, Reproducibility, Climate & Earth System Models, -High Performance Computing

Citation Formats

Pouchard, Line C., Baldwin, Sterling A., Elsethagen, Todd O., Jha, Shantenu, Raju, Bibi, Stephan, Eric G., Tang, Li, and Kleese van Dam, Kerstin. Computational Reproducibility of Scientific Workflows at Extreme Scales. United States: N. p., 2019. Web. doi:10.1177/1094342019839124.
Pouchard, Line C., Baldwin, Sterling A., Elsethagen, Todd O., Jha, Shantenu, Raju, Bibi, Stephan, Eric G., Tang, Li, & Kleese van Dam, Kerstin. Computational Reproducibility of Scientific Workflows at Extreme Scales. United States. doi:10.1177/1094342019839124.
Pouchard, Line C., Baldwin, Sterling A., Elsethagen, Todd O., Jha, Shantenu, Raju, Bibi, Stephan, Eric G., Tang, Li, and Kleese van Dam, Kerstin. Sun . "Computational Reproducibility of Scientific Workflows at Extreme Scales". United States. doi:10.1177/1094342019839124.
@article{osti_1577046,
title = {Computational Reproducibility of Scientific Workflows at Extreme Scales},
author = {Pouchard, Line C. and Baldwin, Sterling A. and Elsethagen, Todd O. and Jha, Shantenu and Raju, Bibi and Stephan, Eric G. and Tang, Li and Kleese van Dam, Kerstin},
abstractNote = {We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: scientific reproducibility of results in the ACME climate simulations and performance reproducibility in molecular dynamics workflows on HPC computing platforms.},
doi = {10.1177/1094342019839124},
journal = {International Journal of High Performance Computing Applications},
number = 5,
volume = 33,
place = {United States},
year = {2019},
month = {9}
}

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