Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean
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Many_Task_Computing_for_Real-Time_Uncertainty_Pred.pdf
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Author(s)
Evangelinos, Constantinos
Lermusiaux, Pierre
Xu, Jinshan
Haley, Patrick
Hill, Christopher N.
Date Issued
February 2011
Journal
IEEE Transactions on Parallel and Distributed Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Evangelinos, C et al. “Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean.” IEEE Transactions on Parallel and Distributed Systems 22, 6 (June 2011): 1012–1024 © 2011 IEEE
Version
Author's final manuscript
Abstract
Uncertainty prediction for ocean and climate predictions is essential for multiple applications today. Many-Task Computing can play a significant role in making such predictions feasible. In this manuscript, we focus on ocean uncertainty prediction using the Error Subspace Statistical Estimation (ESSE) approach. In ESSE, uncertainties are represented by an error subspace of variable size. To predict these uncertainties, we perturb an initial state based on the initial error subspace and integrate the corresponding ensemble of initial conditions forward in time, including stochastic forcing during each simulation. The dominant error covariance (generated via SVD of the ensemble) is used for data assimilation. The resulting ocean fields are used as inputs for predictions of underwater sound propagation. ESSE is a classic case of Many Task Computing: It uses dynamic heterogeneous workflows and ESSE ensembles are data intensive applications. We first study the execution characteristics of a distributed ESSE workflow on a medium size dedicated cluster, examine in more detail the I/O patterns exhibited and throughputs achieved by its components as well as the overall ensemble performance seen in practice. We then study the performance/usability challenges of employing Amazon EC2 and the Teragrid to augment our ESSE ensembles and provide better solutions faster. Keywords: MTC; assimilation; data-intensive; ensemble
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TPDS.2011.64
