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The Computational Efficiency in Mathematical Algorithms

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Pubblicato in:International Journal of Combinatorial Optimization Problems and Informatics vol. 16, no. 2 (2025), p. 191
Autore principale: Eric León Olivares
Altri autori: Márquez Strociak, Luis Carlos, Mayra Lorena González Mosqueda, Karla Martínez Tapia, Salvador Martínez Pagola, Eric Simancas Acevedo
Pubblicazione:
International Journal of Combinatorial Optimization Problems & Informatics
Soggetti:
Systems engineering
Algorithms
Source code
Parallel programming
Multiprocessing
Edge detection
Computational efficiency
Big Data
Optimization
Supercomputers
Measurement techniques
Linear programming
Engineering
Libraries
Informatics
Mathematical problems
Workloads
High performance computing
Efficiency
Comparative analysis
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Abstract:The implementation of mathematical algorithms plays a fundamental role in computational efficiency. Sequential programming, which processes instructions in a linear manner, often struggles with large data volumes due to its inherent limitations. In contrast, parallel programming distributes tasks across multiple cores, significantly reducing processing times and improving overall performance. This paper presents a comparative analysis of both approaches and their relevance in Systems Engineering, where computational optimization is critical. To this end, we implement and evaluate the Sobel algorithm—commonly used for edge detection in images—in both sequential and parallel modes. The implementation is carried out in Python, leveraging the NumPy, OpenCV, and Multiprocessing libraries. This study analyzes the conditions under which parallelization enhances performance and identifies scenarios where process overhead may negate its benefits, thus establishing fundamental criteria for applying these techniques to solve mathematical problems in engineering. The source code is available on GitHub at: [GitHub Repository].
ISSN:2007-1558
DOI:10.61467/2007.1558.2025.v16i2.1081
Fonte:Advanced Technologies & Aerospace Database