PARALLEL COMPUTATION AND ALGORITHMIC APPROACHES IN MANUFACTURING SIMULATION

Abstract

This article explores the application of parallel computing methods and algorithmic approaches in the modeling of manufacturing processes. As manufacturing processes become increasingly complex, involving large datasets and multi-parameter systems, traditional sequential computational methods face limitations. Parallel computing, utilizing multiple processors or cores, offers a solution by accelerating simulations and enabling real-time decision-making. The article reviews key parallel computing technologies, such as MPI, CUDA, and OpenMP, and discusses their implementation in various manufacturing applications, including material flow simulation in production lines, energy consumption forecasting, and product testing. It also examines the development of specialized algorithms, such as data and task parallelism algorithms, to optimize resource use and speed. The results of simulations indicate significant improvements in processing time, resource efficiency, and scalability, although challenges such as synchronization issues and communication costs remain. The findings highlight the potential of parallel computing to enhance the efficiency, competitiveness, and decision-making capabilities of manufacturing systems, while emphasizing the need for further optimization and expert development of parallel algorithms.