| Autor: |
Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen, Matthew Baughman |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
BMC Bioinformatics, Vol 19, Iss S18, Pp 59-69 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2105 |
| DOI: |
10.1186/s12859-018-2508-4 |
| Popis: |
Abstract Background Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines. Results This paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks. Conclusions Initial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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