Executorlib – Up-scaling Python workflows for hierarchical heterogenous high-performance computing
Janssen et al., (2025). Executorlib – Up-scaling Python workflows for hierarchical heterogenous high-performance computing. Journal of Open Source Software, 10(108), 7782, https://doi.org/10.21105/joss.07782
Authors
- Michael G. Taylor (ORCID: https://orcid.org/0000-0003-4327-2746)
- Jörg Neugebauer (ORCID: https://orcid.org/0000-0002-7903-2472)
- Jan Janßen (ORCID: https://orcid.org/0000-0001-9948-7119)
- Danny Pérez (ORCID: https://orcid.org/0000-0003-3028-5249)
- Ping Yang (ORCID: https://orcid.org/0000-0003-4726-2860)
Institutions
- Los Alamos National Laboratory (US)
- Max-Planck-Institut für Nachhaltige Materialien (DE)
Publication Details
- Journal
- The Journal of Open Source Software
- Published
- 2025-04-01
- DOI
- https://doi.org/10.21105/joss.07782
- Citations
- 1
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 4.17
Funders
- U.S. Department of Energy
- Deutsche Forschungsgemeinschaft
- Office of Science
- National Nuclear Security Administration
- Basic Energy Sciences
- Los Alamos National Laboratory