https://hdl.handle.net/10481/15210 Sun, 05 Apr 2026 12:20:05 GMT 2026-04-05T12:20:05Z https://hdl.handle.net/10481/107358 Introduction to Concurrent Programming Capel Tuñón, Manuel Isidoro Introduces concurrent programming, which involves executing multiple processes simultaneously, in contrast to the linear sequence of instructions in sequential programming. It explains key concepts such as: • Processes: Independent execution units that perform tasks concurrently. • Concurrency Benefits: Improves efficiency, especially for tasks with frequent input/ output operations, and allows parallelism even with limited processor cores. • Concurrency Model: Describes how concurrent programs handle synchronization, communication and execution order using techniques like mutual exclusion and synchronization primitives. • Process Creation: Covers methods like fork/join and POSIX threads, which enable dynamic process creation and concurrent task execution. The chapter highlights how concurrent programs better reflect real-world systems where multiple activities happen simultaneously, enhancing program efficiency and responsiveness. https://hdl.handle.net/10481/107358 https://hdl.handle.net/10481/106134 GPU-Accelerated PSO for Neural Network-Based Energy Consumption Prediction Capel Tuñón, Manuel Isidoro; Salguero Hidalgo, Alberto Gabriel; Holgado Terriza, Juan Antonio This study incorporates advanced parallelism methods using GPU computing to accelerate the process of convergence to an objective, providing faster results for Particle Swarm Optimization (PSO), a bio-inspired stochastic optimization algorithm used to make predictions in various fields. The two proposed distributed implementations of PSO with Apache Spark further enable comprehensive optimisation of both the algorithm structure and its parameters, leading to improved predictive accuracy . Therefore, this approach provides a new and inno vative solution in the field of energy consumption prediction, which can be implemented in a distributed edge–computing solution with optimal performance. https://hdl.handle.net/10481/106134 https://hdl.handle.net/10481/89946 Fractal Analysis in MATLAB: A Tutorial for Neuroscientists Ruiz de Miras, Juan MATLAB is one of the software platforms most widely used for scientific computation. MATLAB includes a large set of functions, packages, and toolboxes that make it simple and fast to obtain complex mathematical and statistical computations for many applications. In this chapter, we review some tools available in MATLAB for performing fractal analyses on typical neuroscientific data in a practical way. We provide detailed examples of how to calculate the fractal dimension of 1D, 2D, and 3D data in MATLAB. Furthermore, we review other software packages for fractal analysis. This work is part of the research project PID2019-105145RB-I00 supported by the Spanish Government (MCIN/AEI/10.13039/501100011033). https://hdl.handle.net/10481/89946 https://hdl.handle.net/10481/88852 On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO Villar Castro, Pedro; Fernández, Alberto; Herrera Triguero, Francisco Fuzzy rule-based systems constitute a very spread tool for classi cation problems, but several proposals may decrease its performance when dealing with multi-class problems. Among existing approaches, the FARC-HD algorithm has excelled as it has shown to achieve accurate and compact classi ers, even in the context of multi-class problems. In this work, we aim to go one step further to improve the behavior of the former algorithm by means of a "divide-and-conquer" approach, via binarization in a one-vs-one scheme. Besides, we will contextualize each binary classi er by adapting the data base for each subproblem by means of a granularity learning process to adapt the number of fuzzy labels per variable. Our experimental study, using several data-sets from KEEL data-set repository, shows the goodness of the proposed methodology. https://hdl.handle.net/10481/88852 https://hdl.handle.net/10481/88352 An Introduction to GPU Computing for Numerical Simulation Mantas Ruiz, José Miguel; de la Asunción, Marc; Castro, Manuel Jesús Graphics Processing Units (GPUs) have proven to be a powerful accelerator for intensive numerical computations. The massive parallelism of these platforms makes it possible to achieve dramatic runtime reductions over a standard CPU in many numerical applications at a very affordable price. Moreover, several programming environments, such as NVIDIA’s Compute Unified Device Architecture (CUDA) have shown a high effectiveness in the mapping of numerical algorithms to GPUs. These notes provide an introduction to the development of CUDA programs for numerical simulation using CUDA C/C++, the most popular GPU programming toolkit. An overview of CUDA programming will be illustrated through the CUDA implementation of simple numerical examples for PDEs. These CUDA implementations will be studied and run on modern GPU-based platforms.; Las unidades de procesamiento gráfico (GPU) han demostrado ser un potente acelerador para cálculos numéricos intensivos. El paralelismo masivo de estas plataformas permite conseguir drásticas reducciones del tiempo de ejecución con respecto a una CPU estándar en muchas aplicaciones numéricas a un precio muy asequible. Además, varios entornos de programación, como la plataforma Compute Unified Device Architecture (CUDA) de NVIDIA, han demostrado una gran eficacia en la adaptación de algoritmos numéricos a GPU. Estas notas proporcionan una introducción al desarrollo de programas CUDA para simulación numérica utilizando CUDA C/C++, el kit de herramientas de programación para GPU más popular. Se ilustrará una visión general de la programación en CUDA a través de la implementación en CUDA de sencillos ejemplos numéricos para resolver EDPs. Estas implementaciones CUDA se estudiarán y ejecutarán en modernas plataformas basadas en GPU. https://hdl.handle.net/10481/88352