Benchmarking para procesamiento paralelo : Medida del desempeño de la ejecución paralela utilizando Matlab.

Abstract

In this project, a comprehensive comparative analysis between sequential and parallel image processing is carried out to evaluate their impact in terms of efficiency and speed. The research focuses on the implementation and comparison of edge detection algorithms in images, using both sequential and parallel approaches. In the sequential approach, the conventional implementation of image processing algorithms, which process pixels one after another in a given order, is explored. This method is compared with the parallel approach, which takes advantage of modern hardware resources to process multiple parts of the image simultaneously. Edge detection algorithms are implemented in both modalities and run times, quality of results and overall performance are compared. A diverse set of images, including variations in content and resolution, is used to evaluate the behavior of the approaches in different scenarios. The results obtained from this analysis provide valuable information on the feasibility and advantages of parallel versus sequential image processing. In addition, use cases and applications where one or the other approach may be more beneficial are highlighted. These findings have direct implications for the optimization of image processing algorithms for real- time applications and analysis of large data sets.