Use Cases of Computational Reproducibility for Scientific Workflows at Exascale

Line Pouchard; Sterling Baldwin; Todd Elsethagen; Carlos Gamboa; Shantenu Jha; Bibi Raju; Eric Stephan; Li Tang; Kerstin Kleese Van Dam

Use Cases of Computational Reproducibility for Scientific Workflows at Exascale

Distributed, Parallel, and Cluster Computing 2018-05-04 v1

Authors: Line Pouchard , Sterling Baldwin , Todd Elsethagen , Carlos Gamboa , Shantenu Jha , Bibi Raju , Eric Stephan , Li Tang , Kerstin Kleese Van Dam

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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.

Keywords

computational reproducibility scientific workflows exascale provenance performance metrics climate simulation molecular dynamics

Cite

@article{arxiv.1805.00967,
  title  = {Use Cases of Computational Reproducibility for Scientific Workflows at Exascale},
  author = {Line Pouchard and Sterling Baldwin and Todd Elsethagen and Carlos Gamboa and Shantenu Jha and Bibi Raju and Eric Stephan and Li Tang and Kerstin Kleese Van Dam},
  journal= {arXiv preprint arXiv:1805.00967},
  year   = {2018}
}

Comments

Presented at SC17, Denver, CO. Full version submitted to IJHPCA March 2018

Use Cases of Computational Reproducibility for Scientific Workflows at Exascale

Abstract

Keywords

Cite

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