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    A Fault-Tolerant HPC Scheduler Extension for Large and Operational Ensemble Data Assimilation:Application to the Red Sea

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    Type
    Article
    Authors
    Toye, Habib
    Kortas, Samuel
    Zhan, Peng cc
    Hoteit, Ibrahim cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Beacon Development Company
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    KAUST Supercomputing Laboratory (KSL)
    Physical Science and Engineering (PSE) Division
    Supercomputing, Computational Scientists
    Date
    2018-04-26
    Online Publication Date
    2018-04-26
    Print Publication Date
    2018-07
    Permanent link to this record
    http://hdl.handle.net/10754/627684
    
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    Abstract
    A fully parallel ensemble data assimilation and forecasting system has been developed for the Red Sea based on the MIT general circulation model (MITgcm) to simulate the Red Sea circulation and the Data Assimilation Research Testbed (DART) ensemble assimilation software. An important limitation of operational ensemble assimilation systems is the risk of ensemble members’ collapse. This could happen in those situations when the filter update step imposes large corrections on one, or more, of the forecasted ensemble members that are not fully consistent with the model physics. Increasing the ensemble size is expected to improve the assimilation system performances, but obviously increases the risk of members’ collapse. Hardware failure or slow numerical convergence encountered for some members should also occur more frequently. In this context, the manual steering of the whole process appears as a real challenge and makes the implementation of the ensemble assimilation procedure uneasy and extremely time consuming.This paper presents our efforts to build an efficient and fault-tolerant MITgcm-DART ensemble assimilation system capable of operationally running thousands of members. Built on top of Decimate, a scheduler extension developed to ease the submission, monitoring and dynamic steering of workflow of dependent jobs in a fault-tolerant environment, we describe the assimilation system implementation and discuss in detail its coupling strategies. Within Decimate, only a few additional lines of Python is needed to define flexible convergence criteria and to implement any necessary actions to the forecast ensemble members, as for instance (i) restarting faulty job in case of job failure, (ii) changing the random seed in case of poor convergence or numerical instability, (iii) adjusting (reducing or increasing) the number of parallel forecasts on the fly, (iv) replacing members on the fly to enrich the ensemble with new members, etc.We demonstrate the efficiency of the system with numerical experiments assimilating real satellites sea surface height and temperature observations in the Red Sea.
    Citation
    Toye H, Kortas S, Zhan P, Hoteit I (2018) A Fault-Tolerant HPC Scheduler Extension for Large and Operational Ensemble Data Assimilation:Application to the Red Sea. Journal of Computational Science. Available: http://dx.doi.org/10.1016/j.jocs.2018.04.018.
    Sponsors
    The research reported in this manuscript was supported by King Abdullah University of Science and Technology (KAUST) and Saudi ARAMCO, and made use of the resources of the Supercomputing Core Laboratory of KAUST.
    Publisher
    Elsevier BV
    Journal
    Journal of Computational Science
    DOI
    10.1016/j.jocs.2018.04.018
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S1877750317312905
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jocs.2018.04.018
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; KAUST Supercomputing Laboratory (KSL); Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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