Name
Abstract | ||
|---|---|---|
•Flexible consistency models are designed to boost machine learning algorithms.•HotML provides consistent and flexible checkpoint methods for fault tolerance.•HotML can achieve up to 1.9× performance compared to Petuum. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jocs.2017.09.006 | Journal of Computational Science |
Keywords | Field | DocType |
Big data,Parameter server,Distributed machine learning,Fault tolerance | Online machine learning,Computer science,Workload,Sentiment analysis,Server,Fault tolerance,Artificial intelligence,Consistency model,Big data,Machine learning,Scalability,Distributed computing | Journal |
Volume | ISSN | Citations |
26 | 1877-7503 | 3 |
PageRank | References | Authors |
0.43 | 35 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yangyang Zhang | 1 | 16 | 4.04 |
| Jianxin Li | 2 | 725 | 92.14 |
| Chenggen Sun | 3 | 8 | 1.17 |
| Md. Zakirul Alam Bhuiyan | 4 | 560 | 66.51 |
| weiren yu | 5 | 36 | 3.35 |
| Richong Zhang | 6 | 232 | 39.67 |