Impact synchronization strategies on the performance of parallel processes: a systematic review IMPACT OF SYNCHRONIZATION STRATEGIES ON THE PERFORMANCE OF PARALLEL PROCESSES: A SYSTEMATIC REVIEW

Process synchronization, parallel computing, performance, systematic review,PRISMA methodology

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Keywords:

Process synchronization, parallel computing, performance, systematic review, PRISMA methodology

Abstract

The report addresses the management of parallel processes and synchronization in concurrent systems, highlighting common problems in accessing shared resources that affect efficiency and stability. It also highlights the need to optimize the management of large volumes of data and improve academic cooperation to develop advanced solutions.The objective of the study is to identify and analyze synchronization strategies that optimize parallel processes, evaluating their effectiveness in different scenarios and offering recommendations based on recent literature. A systematic review was conducted using the PRISMA methodology, selecting relevant articles published in the last five years and formulating key questions to guide the analysis. The results highlight that non-blocking strategies are more effective for managing shared resources, and techniques such as the use of mutex and actor models contribute to efficiency and stability in concurrent systems. In addition, visual approaches facilitate the identification of key patterns in the synchronization of parallel processes. The study concludes that it is essential to implement interactive methods to teach concurrent and parallel programming, and to foster academic collaboration to overcome technological challenges. Recommendations emphasize prioritizing effective methodologies according to their applicability and promoting advanced strategies to improve the performance of concurrent systems.

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Author Biography

  • Iván Carlo Petrlik Azabache, UNIVERSIDAD NACIONAL MAYOR DE SAN MARCOS

    Ingeniero de Computación y Sistemas, Doctor y Magister en Ingeniería de Sistemas, Magister en Inteligencia Artificial con registro CIP Nº 91445 del Colegio de Ingenieros del Perú. Docente Universitario ordinario de las Universidad Nacional Mayor de San Marcos  y la Universidad Nacional  Federico Villarreal e Investigador Renacyt  Concytec con Código de Registro: P0108931. Experiencia como analista y programador de software y directivo del capítulo de Ingeniería Industrial, Sistemas y Transportes del Colegio de Ingenieros del Perú. Certificado Internacional en Scrum Master.

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Published

2025-08-22

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