Name
Abstract | ||
|---|---|---|
Recent successes of deep learning have been largely driven by the ability to train large models on vast amounts of data. We believe that High Performance Computing (HPC) will play an increasingly important role in helping deep learning achieve the next level of innovation fueled by neural network models that are orders of magnitude larger and trained on commensurately more training data. We are targeting the unique capabilities of both current and upcoming HPC systems to train massive neural networks and are developing the Livermore Big Artificial Neural Network (LBANN) toolkit to exploit both model and data parallelism optimized for large scale HPC resources. This paper presents our preliminary results in scaling the size of model that can be trained with the LBANN toolkit. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2834892.2834897 | MLHPC@SC |
Field | DocType | Citations |
Training set,Supercomputer,Computer science,Parallel computing,Exploit,Data parallelism,Artificial intelligence,Deep learning,Artificial neural network,Distributed computing | Conference | 17 |
PageRank | References | Authors |
0.80 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Van Essen | 1 | 183 | 15.53 |
| Hyojin Kim | 2 | 22 | 2.66 |
| Roger Pearce | 3 | 244 | 19.40 |
| Kofi Boakye | 4 | 155 | 13.64 |
| Barry Chen | 5 | 25 | 3.42 |