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MLHPC 2016 : Machine Learning in High Performance Computing Environments Workshop

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Link: http://ornlcda.github.io/MLHPC2016/
 
When Nov 14, 2016 - Nov 14, 2016
Where Salt Lake City, UT, USA
Submission Deadline Aug 31, 2016
Notification Due Sep 22, 2016
Final Version Due Sep 30, 2016
Categories    machine learning   deep learning   high performance computing   supercomputing
 

Call For Papers

Call for Papers

The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for machine learning. This workshop will focus on the greatest challenges in utilizing HPC for machine learning and methods for exploiting data parallelism, model parallelism, ensembles, and parameter search. We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning and to share the wide range of applications that would benefit from HPC powered machine learning.

In recent years, the models and data available for machine learning (ML) applications have grown dramatically. High performance computing (HPC) offers the opportunity to accelerate performance and deepen understanding of large data sets through machine learning. Current literature and public implementations focus on either cloud-­‐based or small-­‐scale GPU environments. These implementations do not scale well in HPC environments due to inefficient data movement and network communication within the compute cluster, originating from the significant disparity in the level of parallelism. Additionally, applying machine learning to extreme scale scientific data is largely unexplored. To leverage HPC for ML applications, serious advances will be required in both algorithms and their scalable, parallel implementations.
Topics will include but will not be limited to:

Machine learning models, including deep learning, for extreme scale systems
Enhancing applicability of machine learning in HPC (e.g. feature engineering, usability)
Learning large models/optimizing hyper parameters (e.g. deep learning, representation learning)
Facilitating very large ensembles in extreme scale systems
Training machine learning models on large datasets and scientific data
Overcoming the problems inherent to large datasets (e.g. noisy labels, missing data, scalable ingest)
Applications of machine learning utilizing HPC
Future research challenges for machine learning at large scale.
Large scale machine learning applications

Authors are invited to submit full papers with unpublished, original work of not more than 8 pages. All papers should be formatted using the ACM style (see http://www.acm.org/sigs/publications/proceedings-templates). All accepted papers (subject to post-review revisions) will be published in the ACM digital and IEEE Xplore libraries by ACM SIGHPC. Papers should be submitted using EasyChair at: https://www.easychair.org/conferences/?conf=mlhpc2016.

This workshop is being held at SC16. http://sc16.supercomputing.org/

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