...  ...  @@ 4,10 +4,24 @@ Every structure should divide the information that they store across processors, 


* Data driven decomposition



* Fixed decomposition






The fist approach try to generate a model for the interaction across processors and try to minimize the space



The fist approach try to create a model for the interactions across processors and try to minimize the space






The second instead try to divide the quantity of information the structure store without considering the interactions






## Model decomposition






In a distributedmemory setting, where data are scattered across



processors, two factors are important: equal division of work across



processors and reduction of the communication overhead.



A typical approach, is to formulate the problem as a



graphpartitioning problem: the physical domain is divided into subdomains



(vertices of the graph), each of them carrying a weight modeling the computational cost.



The communication pattern between subdomains is represented as links



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



the communication cost. The requirement of balanced computation with



minimal communication then translates to the 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. Even if a model decomposition



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






The modular structure of OpenFPM give the possibility to create distributed structure with different decomposition strategies. 


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* CartDecomposition 


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