Name
Abstract | ||
|---|---|---|
Exascale computers will offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. These software combinations and integrations, however, are difficult to achieve due to challenges of coordination and deployment of heterogeneous software components on diverse and massive platforms. ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WORKS54523.2021.00012 | 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS) |
Keywords | DocType | ISBN |
Computers,Conferences,Computational modeling,Software | Conference | 978-1-6654-1136-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
15 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aymen Al-Saadi | 1 | 0 | 0.34 |
| Dong H. Ahn | 2 | 0 | 0.68 |
| Yadu Babuji | 3 | 3 | 3.09 |
| Kyle Chard | 4 | 515 | 56.70 |
| James Corbett | 5 | 290 | 12.03 |
| Mihael Hategan | 6 | 573 | 32.86 |
| Stephen Herbein | 7 | 0 | 0.34 |
| Shantenu Jha | 8 | 0 | 0.68 |
| Daniel Laney | 9 | 0 | 0.68 |
| André Merzky | 10 | 2 | 1.39 |
| Todd Munson | 11 | 0 | 0.34 |
| Michael Salim | 12 | 6 | 0.75 |
| Mikhail Titov | 13 | 3 | 1.75 |
| Matteo Turilli | 14 | 2 | 1.05 |
| Justin M. Wozniak | 15 | 464 | 35.32 |