Nowadays in computer modeling for getting results scientists need to spend a lot of time for computing. For example it can be in the case of getting accurate results or in generating dataset for machine learning, when we need to execute a simulation model on a large set of input parameters. Tools and languages selected for model development depend mostly on the team knowledge and background. While some models may be implemented in an efficient manner in C or C++, nowadays more models tend to be written in Python or R, and their execution will be much slower. Other mathematical models may require external tools like Matlab or Wolfram Mathematica, and simulation models usually require their own tools like NS-3 or OMNeT++. If computation takes place on the researcher's computer with limited resources, it can take a long time to get precise results. To increase computing time we decided to implement the system for parallel distributed computing and to investigate performance of computing tasks in the system.