Parallel and Distributed Computing for High-Performance Applications
The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel...
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| Vydané v: | E3S web of conferences Ročník 399; s. 4039 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article Konferenčný príspevok.. |
| Jazyk: | English |
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Les Ulis
EDP Sciences
01.01.2023
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| ISSN: | 2267-1242, 2555-0403, 2267-1242 |
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| Abstract | The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strategy enables quicker execution times and enhanced performance in general. Parallel and distributed computing are essential for high-performance applications like scientific simulations, data analysis, and artificial intelligence since they frequently call for significant computational resources. High-performance apps are able to effectively handle computationally demanding tasks thanks in large part to parallel and distributed computing. This article offers a thorough review of the theories, methods, difficulties, and developments in parallel and distributed computing for high-performance applications. Researchers and practitioners may fully utilize the potential of parallel and distributed computing to open up new vistas in computational science and engineering by comprehending the underlying concepts and utilizing the most recent breakthroughs. |
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| AbstractList | The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strategy enables quicker execution times and enhanced performance in general. Parallel and distributed computing are essential for high-performance applications like scientific simulations, data analysis, and artificial intelligence since they frequently call for significant computational resources. High-performance apps are able to effectively handle computationally demanding tasks thanks in large part to parallel and distributed computing. This article offers a thorough review of the theories, methods, difficulties, and developments in parallel and distributed computing for high-performance applications. Researchers and practitioners may fully utilize the potential of parallel and distributed computing to open up new vistas in computational science and engineering by comprehending the underlying concepts and utilizing the most recent breakthroughs. |
| Author | Vizhi, R. Malar Chandrakala, P. Senthilkumar, R Vijayaraj, M Alzubaidi, Laith H. Muzaffar, Khasanov |
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| Cites_doi | 10.17762/ijritcc.v11i4s.6314 |
| ContentType | Journal Article Conference Proceeding |
| Copyright | 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1051/e3sconf/202339904039 |
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| References | Liu (R19) 2018; 39 Baklizi (R22) 2023; 11 Chen (R17) 2018; 42 Liu (R9) 2019; 40 Chen (R5) 2018; 36 Gupta (R3) 2020; 50 Huang (R12) 2019; 38 Gao (R6) 2019; 45 Yang (R13) 2020; 47 Wu (R11) 2018; 45 Sun (R20) 2019; 41 R24 Wang (R18) 2020; 50 Park (R10) 2017; 42 Zhang (R8) 2018; 43 Brown (R2) 2019; 30 Zhang (R15) 2017; 35 Diniesh (R21) 2023; 11 Wang (R7) 2020; 32 Smith (R1) 2018; 42 Li (R16) 2019; 30 Lee (R4) 2017; 39 Pande (R25) 2022; 2 Sarangi (R23) 2022; 3 Liu (R14) 2018; 51 |
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