Abstract
In the present work we employ a load balancing scheme involving an adaptive and dynamic workload redistribution both along Space and Time directions for solving Data Assimilation problems where the observations are non-uniformly distributed, general sparse and its distribution changes during the time. We will consider the Constrained Least Square model (CLS) as prototype of Data Assimilation problems and we will validate the proposed approach on different configurations. Validation is performed using Parallel Computing Toolbox of MATLABR2013a on high performance hybrid computing architecture.
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