CSC/ECE 506 Fall 2007/wiki2 4 md: Difference between revisions
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== Assignment == | == Assignment == | ||
Once the algorithm is completed and split into different parts, it is then partitioned into different complexes. The ''s'' points of ''D'' are partitioned into ''k'' different complexes {C1 ... Ck}. Each different complex contains ''m'' points. The first complex contains every k(j-1)+1 point of ''D'', the second contains k(j-1)+2 and so on (where j = 1,...m). | |||
== Orchestration == | == Orchestration == | ||
Revision as of 03:23, 25 September 2007
Parallel Application: Shuffled Complex Evolution Metropolis
Overview
The scope of environmental sciences increases significantly each year. With the advancement of computer systems, more intricate (and thus realistic) models are developed every year. As with in advancement in technology, the number of parameters for computation increase each year as well. The Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm is used for global optimization of the estimation of environmental models. This algorithm is then implemented in a parallel in a very user-friendly way for further optimization. The algorithm can be used for several model case studies, such as the prediction of migratory bird flight paths.
Steps of Parallelization
Flow Chart Overview of Parallelization
Decomposition
Assignment
Once the algorithm is completed and split into different parts, it is then partitioned into different complexes. The s points of D are partitioned into k different complexes {C1 ... Ck}. Each different complex contains m points. The first complex contains every k(j-1)+1 point of D, the second contains k(j-1)+2 and so on (where j = 1,...m).