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.
Revised: December 5, 2019 |
Published: September 1, 2019
Citation
Pouchard L.C., S.A. Baldwin, T.O. Elsethagen, S. Jha, B. Raju, E.G. Stephan, and L. Tang, et al. 2019.Computational Reproducibility of Scientific Workflows at Extreme Scales.International Journal of High Performance Computing Applications 33, no. 5:763-776.PNNL-SA-137882.doi:10.1177/1094342019839124