Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses

C Fortran Python Submitted 17 February 2023Published 16 June 2023
Review

Editor: @diehlpk (all papers)
Reviewers: @acrlakshman (all reviews), @NoujoudNader (all reviews)

Authors

Marc Schouler (0000-0002-3708-4135), Robert Alexander Caulk (0000-0001-5618-8629), Lucas Meyer (0000-0001-5386-5997), Théophile Terraz, Christoph Conrads, Sebastian Friedemann, Achal Agarwal (0000-0002-3216-4769), Juan Manuel Baldonado, Bartłomiej Pogodziński, Anna Sekuła (0000-0003-3524-3160), Alejandro Ribes, Bruno Raffin

Citation

Schouler et al., (2023). Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses. Journal of Open Source Software, 8(86), 5291, https://doi.org/10.21105/joss.05291

@article{Schouler2023, doi = {10.21105/joss.05291}, url = {https://doi.org/10.21105/joss.05291}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {86}, pages = {5291}, author = {Schouler, Marc and Caulk, Robert Alexander and Meyer, Lucas and Terraz, Théophile and Conrads, Christoph and Friedemann, Sebastian and Agarwal, Achal and Baldonado, Juan Manuel and Pogodziński, Bartłomiej and Sekuła, Anna and Ribes, Alejandro and Raffin, Bruno}, title = {Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses}, journal = {Journal of Open Source Software} }
Copy citation string · Copy BibTeX  
Tags

supercomputing sensitivity analysis deep learning distributed systems orchestration

Altmetrics
Markdown badge

 

License

Authors of JOSS papers retain copyright.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License

Table of Contents
Public user content licensed CC BY 4.0 unless otherwise specified.
ISSN 2475-9066