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LBANN: livermore big artificial neural network HPC toolkit
| Content Provider | ACM Digital Library |
|---|---|
| Author | Kim, Hyojin Chen, Barry Pearce, Roger Boakye, Kofi Van Essen, Brian |
| 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. |
| Starting Page | 1 |
| Ending Page | 6 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450340069 |
| DOI | 10.1145/2834892.2834897 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-11-15 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Deep learning High performance computing Artificial neural networks |
| Content Type | Text |
| Resource Type | Article |
