Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation

¹ Kazan State Power Engineering University, Kazan, Russia
² Kadyrov Chechen State University, Grozny, Russia

^* Corresponding author: zarim@rambler.ru

Abstract

This article delves into the burgeoning integration of Artificial Intelligence (AI) in parallel programming, highlighting its potential to transform the landscape of computational efficiency and developer experience. We begin by exploring the fundamental role of parallel programming in modern computing and the inherent challenges it presents, such as task distribution, synchronization, and memory management. The advent of AI, especially in machine learning and deep learning, offers novel solutions to these challenges. We discuss the application of AI in automating the creation of parallel programs, with a focus on automatic code generation, adaptive resource management, and the enhancement of developer experience. The article examines specific AI methods – genetic algorithms, reinforcement learning, and neural networks – and their application in optimizing various aspects of parallel programming. Further, we delve into the prospects of combining these AI methods for a synergistic effect, emphasizing the potential for increased efficiency and accuracy. The importance of integrating AI technologies with existing development tools is also highlighted, aiming to bring AI's benefits to a broader developer audience. The article concludes with an outlook on future research directions, including the development of adaptable AI models tailored to diverse tasks and environments in parallel programming. These advancements promise to make parallel programming more powerful, accessible, and efficient, paving the way for a new era of computational capability and innovation.