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SCHEDULE: NOV 16-21, 2014
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NUMARCK: Machine Learning Algorithm for Resiliency and Checkpointing
SESSION: Machine Learning and Data Analytics
EVENT TYPE: Papers
TIME: 10:30AM - 11:00AM
SESSION CHAIR: Hank Childs
AUTHOR(S):Zhengzhang Chen, Seung Woo Son, William Hendrix, Ankit Agrawal, Wei-keng Liao, Alok Choudhary
ROOM:388-89-90
ABSTRACT:
Data checkpointing is an important fault tolerance technique in High Performance Computing systems. This paper exploits the fact that in many scientific applications, relative change in data values from one simulation iteration to the next are not very significantly different from each other. Thus, capturing the distribution of relative changes in data instead of storing data itself allows us to incorporate the temporal dimension of the data, and learn evolving distribution of the changes. We show that an order of magnitude data reduction becomes achievable with a user-defined and guaranteed error bounds for each data point. We propose NUMARCK, NU Machine learning Algorithm for Resiliency and ChecKpointing, that makes use of the emerging distributions of data changes between consecutive simulation iterations, and encodes them into an indexing space that can be concisely represented. We evaluate NUMARCK using two production scientific simulations, FLASH and CMIP5, and demonstrate a superior performance.
Chair/Author Details:
Hank Childs (Chair) - University of Oregon and Lawrence Berkeley National Laboratory
Zhengzhang Chen - Northwestern University
Seung Woo Son - Northwestern University
William Hendrix - Northwestern University
Ankit Agrawal - Northwestern University
Wei-keng Liao - Northwestern University
Alok Choudhary - Northwestern University
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