High dimension CNF to DNF conversion using grid computing

CNF to DNF Conversion is considered as vast area of research by scientists for PLA's, circuit designs, FPGA's, artificial intelligence, etc. High dimension variable conversion has become a key demand in the current business standard. Various applications are in its requirement like gnome analysis, grid computing, bioinformatics, imaging system, rough sets requires higher variable processing algorithm. Problem statement is — Design and implementation of High dimension optimal conjunctive normal form to optimal (prime implicants) disjunctive normal form conversion which is an “NP hard problem conversion to an NP complete”. Thus CNF to DNF can only be considered to evaluate best performance for higher variable processing on high end systems. The best-known representations of Boolean functions f are those as disjunctions of terms (DNFs) and as conjunctions of clauses (CNFs). It is convenient to define the DNF size of f as the minimal number of terms in a DNF representing f and the CNF size as the minimal number of clauses in a CNF representing f.