Name
Abstract | ||
|---|---|---|
Scientific machine learning (SciML) promises to have a transformational impact on scientific exploration, by combining state-of-the-art AI methods with the latest generation of supercomputers. However, to efficiently leverage ML techniques on high-performance computing (HPC) systems, it is critical to understand the performance characteristics of the underlying algorithms on modern computational s... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/PMBS54543.2021.00007 | 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) |
Keywords | DocType | ISBN |
deep learning,high performance computing,scientific machine learning,performance benchmarking | Conference | 978-1-6654-1118-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khaled Z. Ibrahim | 1 | 0 | 0.34 |
| Tan Nguyen | 2 | 0 | 0.34 |
| Hai Ah Nam | 3 | 0 | 0.34 |
| Wahid Bhimji | 4 | 0 | 0.34 |
| Steven Farrell | 5 | 0 | 0.34 |
| Leonid Oliker | 6 | 0 | 0.34 |
| Michael Rowan | 7 | 0 | 0.34 |
| Nicholas J. Wright | 8 | 0 | 0.34 |
| Samuel Williams | 9 | 0 | 0.34 |