Abstract
Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2022 lightning talks, which cover five broad topics: data integrity of scientific workflows; a machine learning-based recommendation system; a Python toolkit for running dynamic ensembles of simulations; a cross-platform, high-performance computing utility for processing shell commands; and a meta(data) framework for reproducing hybrid workflows.
| Original language | English |
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| Title of host publication | Proceedings of WORKS 2022 |
| Subtitle of host publication | 17th Workshop on Workflows in Support of Large-Scale Science, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 84-92 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781665451918 |
| DOIs | |
| State | Published - 2022 |
| Event | 17th IEEE/ACM Workshop on Workflows in Support of Large-Scale Science, WORKS 2022 - Dallas, United States Duration: Nov 13 2022 → Nov 18 2022 |
Publication series
| Name | Proceedings of WORKS 2022: 17th Workshop on Workflows in Support of Large-Scale Science, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis |
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Conference
| Conference | 17th IEEE/ACM Workshop on Workflows in Support of Large-Scale Science, WORKS 2022 |
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| Country/Territory | United States |
| City | Dallas |
| Period | 11/13/22 → 11/18/22 |
Funding
The submitted manuscript has been created in part by 1) Brookhaven Science Associates, LLC operator of Brookhaven National Laboratory, a U.S Department of Energy Office of Science laboratory operated under Contract No. DESC0012704, 2) by UChicago Argonne, LLC, Operator of Argonne National Laboratory, a U.S. Department of Energy Office of Science laboratory, operated under Contract No. DE-AC02-06CH11357, and 3) UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work is partly funded by NSF award OAC-1839900. This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. libEnsemble was developed as part of the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the OLCF at ORNL, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC05-00OR22725.
Keywords
- FAIR
- data integrity
- ensembles
- high performance computing
- machine learning
- scientific workflows