In this article we consider the inversion problem for polynomially computable discrete functions. These functions describe behavior of many discrete systems and are used in model checking, hardware verification, cryptanalysis, computer biology and other domains. Quite often it is necessary to invert these functions, i.e. to find an unknown preimage if an image and algorithm of function computation are given. In general case this problem is computationally intractable. However, many of it's special cases are very important in practical applications. Thus development of algorithms that are applicable to these special cases is of importance. The practical applicability of such algorithms can be validated by their ability to solve the problems that are considered to be computationally hard (for example cryptanalysis problems). In this article we propose the technology of solving the inversion problem for polynomially computable discrete functions. This technology was implemented in distributed computing environments (parallel clusters and Grid-systems). It is based on reducing the inversion problem for the considered function to some SAT problem. We describe a general approach to coarse-grained parallelization for obtained SAT problems. Efficiency of each parallelization scheme is determined by the means of a special predictive function. The proposed technology was validated by successful solving of cryptanalysis problems for some keystream generators. The main practical result of this work is a complete cryptanalysis of keystream generator A5/1 which was performed in a Grid system specially built for this task.