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Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean
June 2011 (vol. 22 no. 6)
pp. 1012-1024
ASCII Text
x 
 
Constantinos Evangelinos, Pierre F.J. Lermusiaux, Jinshan Xu, Patrick J. Haley, Chris N. Hill, "Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 6, pp. 1012-1024, June, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2011.64,
author = {Constantinos Evangelinos and Pierre F.J. Lermusiaux and Jinshan Xu and Patrick J. Haley and Chris N. Hill},
title = {Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {22},
number = {6},
issn = {1045-9219},
year = {2011},
pages = {1012-1024},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2011.64},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean
IS - 6
SN - 1045-9219
SP1012
EP1024
EPD - 1012-1024
A1 - Constantinos Evangelinos,
A1 - Pierre F.J. Lermusiaux,
A1 - Jinshan Xu,
A1 - Patrick J. Haley,
A1 - Chris N. Hill,
PY - 2011
KW - MTC
KW - assimilation
KW - data-intensive
KW - ensemble.
VL - 22
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Constantinos Evangelinos, Massachusetts Institute of Technology, Cambridge
Pierre F.J. Lermusiaux, Massachusetts Institute of Technology, Cambridge
Jinshan Xu, Massachusetts Institute of Technology, Cambridge
Patrick J. Haley, Massachusetts Institute of Technology, Cambridge
Chris N. Hill, Massachusetts Institute of Technology, Cambridge
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.

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Index Terms:
MTC, assimilation, data-intensive, ensemble.
Citation:
Constantinos Evangelinos, Pierre F.J. Lermusiaux, Jinshan Xu, Patrick J. Haley, Chris N. Hill, "Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 6, pp. 1012-1024, June 2011, doi:10.1109/TPDS.2011.64
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