...  ...  @@ 14,9 +14,11 @@ Every structure should divide the information that they store across processors, 


* Data driven decomposition



* Divide the total information in equal chunks






The first approach try to create a model for computation and comunication and try to find a decomposition that minimize it.



The Model decomposition approach try to model the computation and comunication across processor given a certain decomposition and try to find the decomposition that minimize the communication balancing the computation.






The second just divide the domain as the user specified and assign 1 subdomain to each processor



The Fixed decomposition just divide the domain as the user specified and assign 1 subdomain to each processor






The data driven decomposition instead consider the volume of the information the structure store and divide the volume equally them equally regardless of the communication.






### Model decomposition




...  ...  @@ 29,11 +31,11 @@ graphpartitioning problem: the physical domain is divided into subsubdomains 


The communication pattern between subdomains is represented as links



between the subsubdomains (edges of the graph) with weights formalizing



the communication cost (_Generate graph model_). The requirement of balanced computation with



minimal communication then translates to the optimization problem of



minimal communication then translates to an optimization problem of



finding a graph partitioning where each group contains the same sum of



weights, and the sum of the cut edges is minimal (_Find optimal decomposition_). As final step



the decomposition can be postprocess further more to be for optimal, based on factors not considered by the optimization process, like merging vertex, or merging subsubdomain, to create bigger subdomains.



Even if a model decomposition it is not bind to a graph model it is true that until now it is the main approach



the decomposition can be postprocess further more to be more optimal and based on factors not considered by the optimization process, like merging vertex, or merging subsubdomain, to create bigger subdomains.



Even if a model decomposition it is not bind to a graph model it is true that until now it is the best approach in scientific computation






* CartDecomposition [(doc)](CartDecomposition)[(API reference)](http://ppmcore.mpicbg.de/doxygen/openfpm_pdata/classCartDecomposition.html)




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