Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation
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 comput...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/97/e3sconf_bft2023_04017.pdf |
| _version_ | 1797345165086556160 |
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| author | Zaripova Rimma Mentsiev Adam Kovrizhnykh Olga |
| author_facet | Zaripova Rimma Mentsiev Adam Kovrizhnykh Olga |
| author_sort | Zaripova Rimma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:13:33Z |
| format | Article |
| id | doaj.art-904ec4b8f60346ae8b02daaa6150050e |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T11:13:33Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-904ec4b8f60346ae8b02daaa6150050e2024-01-26T10:39:29ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014600401710.1051/e3sconf/202346004017e3sconf_bft2023_04017Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automationZaripova Rimma0Mentsiev Adam1Kovrizhnykh Olga2Kazan State Power Engineering UniversityKadyrov Chechen State UniversityKazan State Power Engineering UniversityThis 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/97/e3sconf_bft2023_04017.pdf |
| spellingShingle | Zaripova Rimma Mentsiev Adam Kovrizhnykh Olga Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation E3S Web of Conferences |
| title | Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation |
| title_full | Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation |
| title_fullStr | Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation |
| title_full_unstemmed | Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation |
| title_short | Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation |
| title_sort | advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/97/e3sconf_bft2023_04017.pdf |
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