DEVELOPMENT AND EXPLORATION OF PARALLEL TECHNOLOGIES IN STOCHASTIC PROGRAMMING TASKS

This research examines parallel technologies for modeling tasks using the Monte-Carlo method. The actuality of these studies is explained by the fact that the Monte-Carlo method has had and continues to have a significant impact on the development of computational mathematics. It is shown that the main essence of the method lies in the random simulation of a large number of scenarios and statistical processing of the results, which explains the inherent possibility of its parallelization. It is noted that since individual iterations of the Monte-Carlo method are typically independent of one another, they can be easily distributed among several threads or nodes of a cluster system. This makes the method ideal for parallel and distributed computing. The main aim of the research is to highlight peculiarities of par-allelizing computations in solving a wide range of applied tasks. Calculation schemes that ensure increased performance and speed are presented. The effectiveness of the proposed ap-proach is illustrated by studies and graphical interpretations of convergence and approxima-tion of the developed approach.