Name
Abstract | ||
|---|---|---|
Artificial Intelligence (AI) is being adopted in different domains at an unprecedented scale. A significant interest in the scientific community also involves leveraging machine learning (ML) to effectively run high performance computing applications at scale. Given multiple efforts in this arena, there are often duplicated efforts when existing rich data sets and ML models could be leveraged inst... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MLHPC54614.2021.00011 | 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) |
Keywords | DocType | ISBN |
Codes,Computational modeling,Biological system modeling,High performance computing,Conferences,Ecosystems,Machine learning | Conference | 978-1-6654-1124-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaurav Verma | 1 | 0 | 0.68 |
| Murali Emani | 2 | 2 | 2.39 |
| Chunhua Liao | 3 | 330 | 30.72 |
| Pei-Hung Lin | 4 | 35 | 8.34 |
| Tristan Vanderbruggen | 5 | 1 | 1.36 |
| Xipeng Shen | 6 | 2025 | 118.55 |
| Barbara Chapman | 7 | 0 | 0.68 |