Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean
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| Title: | Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean |
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| Authors: | Evangelinos, Constantinos, Lermusiaux, Pierre F., Xu, Jinshan, Haley, Jr, Patrick J., Hill, Chris N. |
| Contributors: | MASSACHUSETTS INST OF TECH CAMBRIDGE DEPT OF EARTH ATMOSPHERIC AND PLANETARY SCIENCES |
| Source: | DTIC |
| Publication Year: | 2010 |
| Collection: | Defense Technical Information Center: DTIC Technical Reports database |
| Subject Terms: | Physical and Dynamic Oceanography, ASSIMILATION, UNCERTAINTY, OCEAN MODELS, REAL TIME, CLUSTERING, PERTURBATION THEORY, HETEROGENEITY, COVARIANCE, WEATHER FORECASTING, INPUT OUTPUT PROCESSING, STOCHASTIC PROCESSES, COMPUTATIONS, CODING, MTC(MANY TASK COMPUTING), DATA-INTENSIVE, ENSEMBLE, ESSE(ERROR SUBSPACE STATISTICAL ESTIMATION), ACOUSTIC MODELING |
| Description: | Error Subspace Statistical Estimation (ESSE), the uncertainty prediction and data assimilation methodology employed for real-time ocean forecasts, is based on a characterization and prediction of the largest uncertainties. This is carried out by evolving an error subspace of variable size. We use an ensemble of stochastic model simulations, initialized based on an estimate of the dominant initial uncertainties, to predict the error subspace of the model fields. The dominant error covariance (generated via an SVD of the ensemble generated error covariance matrix) is used for data assimilation. The resulting ocean fields are provided as the input to acoustic modeling, allowing for the prediction and study of the spatiotemporal variations in acoustic propagation. The ESSE procedure is a classic case of Many Task Computing: These codes are managed based on dynamic workflows for (1) the perturbation of the initial mean state, (2) the subsequent ensemble of stochastic PE model runs, (3) the continuous generation of the covariance matrix, (4) the successive computations of the SVD of the ensemble spread until a convergence criterion is satisfied, and (5) the data assimilation. Its ensemble nature makes it a many task data intensive application and its dynamic workflow gives it heterogeneity. Subsequent acoustics propagation modeling involves a very large ensemble of very short in duration acoustics runs. We study the execution characteristics and challenges of a distributed ESSE workflow on a large dedicated cluster and the usability of enhancing this with runs on Amazon EC2 and the Teragrid and the I/O challenges faced. ; Submitted for publication in the IEEE Transactions on Parallel and Distributed Systems Journal, A Special Issue on Many-Task Computing. The original document contains color images. Sponsored in part by NSF and ONR under N00014-08-1-0586. |
| Document Type: | text |
| File Description: | text/html |
| Language: | English |
| Relation: | http://www.dtic.mil/docs/citations/ADA513019 |
| Availability: | http://www.dtic.mil/docs/citations/ADA513019 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA513019 |
| Rights: | Approved for public release; distribution is unlimited. |
| Accession Number: | edsbas.F7722D5B |
| Database: | BASE |
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