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Scalable Parallel Processing: Architectural Models, Real-Time Programming, and Performance Evaluation †.

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Τίτλος: Scalable Parallel Processing: Architectural Models, Real-Time Programming, and Performance Evaluation †.
Συγγραφείς: Sino, Mirela, Domazet, Ervin
Πηγή: Engineering Proceedings; 2025, Vol. 104 Issue 1, p60, 8p
Θεματικοί όροι: Parallel processing, Real-time programming, Benchmark problems (Computer science), SIMD (Computer architecture), Adaptive computing systems
Περίληψη: This research paper analyzes and highlights the benefits of parallel processing to enhance performance and computational efficiency in modern computing systems. It explores two primary models of parallelism—single instruction, multiple data (SIMD) and multiple instruction, multiple data (MIMD)—by examining their architectures and real-world use cases such as artificial intelligence, image processing, and cloud computing. Special emphasis is placed on the role of parallel programming in real-time systems, with a focus on APIs such as OpenMP and Ada, which facilitate structured parallelism. To demonstrate the practical advantages of parallelism, a comparative study is presented between a parallel merge-sort algorithm and its serial counterpart. Experimental analysis across datasets ranging from 100,000 to 1,000,000 elements shows that execution time can be reduced by up to 60–70% when using eight-core parallelization compared to serial execution. These results illustrate the scalability and effectiveness of parallel processing in handling large-scale computations. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Proceedings is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Scalable Parallel Processing: Architectural Models, Real-Time Programming, and Performance Evaluation †.
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  Data: <searchLink fieldCode="AR" term="%22Sino%2C+Mirela%22">Sino, Mirela</searchLink><br /><searchLink fieldCode="AR" term="%22Domazet%2C+Ervin%22">Domazet, Ervin</searchLink>
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  Data: Engineering Proceedings; 2025, Vol. 104 Issue 1, p60, 8p
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  Data: <searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+programming%22">Real-time programming</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22SIMD+%28Computer+architecture%29%22">SIMD (Computer architecture)</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+computing+systems%22">Adaptive computing systems</searchLink>
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  Data: This research paper analyzes and highlights the benefits of parallel processing to enhance performance and computational efficiency in modern computing systems. It explores two primary models of parallelism—single instruction, multiple data (SIMD) and multiple instruction, multiple data (MIMD)—by examining their architectures and real-world use cases such as artificial intelligence, image processing, and cloud computing. Special emphasis is placed on the role of parallel programming in real-time systems, with a focus on APIs such as OpenMP and Ada, which facilitate structured parallelism. To demonstrate the practical advantages of parallelism, a comparative study is presented between a parallel merge-sort algorithm and its serial counterpart. Experimental analysis across datasets ranging from 100,000 to 1,000,000 elements shows that execution time can be reduced by up to 60–70% when using eight-core parallelization compared to serial execution. These results illustrate the scalability and effectiveness of parallel processing in handling large-scale computations. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Proceedings is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/engproc2025104060
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        Text: English
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      – SubjectFull: Parallel processing
        Type: general
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      – SubjectFull: Benchmark problems (Computer science)
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      – SubjectFull: SIMD (Computer architecture)
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      – SubjectFull: Adaptive computing systems
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              Text: 2025
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