Person: Igual Peña, Francisco Daniel
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First Name
Francisco Daniel
Last Name
Igual Peña
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
Identifiers
11 results
Search Results
Now showing 1 - 10 of 11
Item Revisiting Conventional Task Schedulers to Exploit Asymmetry in ARM big.LITTLE Architectures for Dense Linear Algebra(Parallel Computing, 2017) Costero Valero, Luis María; Igual Peña, Francisco Daniel; Olcoz Herrero, Katzalin; Catalán Pallarés, Sandra; Rodríguez Sánchez, Rafael; Quintana-Ortí, Enrique S.Dealing with asymmetry in the architecture opens a plethora of questions related with the performance- and energy-efficient scheduling of task-parallel applications. While there exist early attempts to tackle this problem, for example via ad-hoc strategies embedded in a runtime framework, in this paper we take a different path, which consists in addressing the asymmetry at the library-level by developing a few asymmetry-aware fundamental kernels. The appealing consequence is that the architecture heterogeneity remains then hidden from the task scheduler. In order to illustrate the advantage of our approach, we employ two well-known matrix factorizations, key to the solution of dense linear systems of equations. From the perspective of the architecture, we consider two low-power processors, one of them equipped with ARM big.LITTLE technology; furthermore, we include in the study a different scenario, in which the asymmetry arises when the cores of an Intel Xeon server operate at two distinct frequencies. For the specific domain of dense linear algebra, we show that dealing with asymmetry at the library-level is not only possible but delivers higher performance than a naive approach based on an asymmetry-oblivious scheduler. Furthermore, this solution is also competitive in terms of performance compared with an ad-hoc asymmetry-aware scheduler furnished with sophisticated scheduling techniques.Item Robust motion estimation on a low-power multi-core DSP(Eurasip journal on advances in signal processing, 2013) Igual Peña, Francisco Daniel; Botella Juan, Guillermo; García Sánchez, Carlos; Prieto Matías, Manuel; Tirado Fernández, José FranciscoMedical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.- Project number: 172
Item Integración de los servicios para.TI@UCM en una plataforma de e-learning similar al Campus Virtual(2014) Sánchez-Elez Martín, Marcos; Risco Martín, José Luis; Pardines Lence, María Inmaculada; Garnica Alcázar, Antonio Óscar; Miñana Ropero, María Guadalupe; Gómez Pérez, José Ignacio; Olcoz Herrero, Katzalin; Chaver Martínez, Daniel Ángel; Castro Rodríguez, Fernando; Sáez Alcaide, Juan Carlos; Igual Peña, Francisco DanielLa integración de los servicios para.TI@UCM en nuestra Universidad hace plantearnos nuevas metodologías docentes y de evaluación en el proceso de enseñanza-aprendizaje. Este proyecto surge como continuación del proyecto PIMCD UCM 138 (2013) titulado “Uso de los servicios para.TI@UCM para integrar tareas docentes y fomentar el aprendizaje activo y colaborativo de los alumnos” desarrollado por este mismo grupo de profesores. Como resultado de este proyecto se han elaborado una serie de tutoriales sobre el uso de las aplicaciones de Google en el ámbito de las tareas docentes como herramientas útiles para fomentar el aprendizaje de los alumnos. Partiendo del nuevo marco docente creado en el PIMCD UCM 138 (2013) donde tanto el material docente como las actividades propuestas a los alumnos se desarrollan en la nube, el objetivo de este nuevo proyecto es conseguir integrar todas las aplicaciones necesarias para un desarrollo completo de la actividad docente en la nube (para.TI@UCM), tanto las propietarias de Google como las desarrolladas por terceros. Nuestro objetivo es intentar crear una plataforma de e-learning similar al Campus Virtual. Para realizar esta tarea será necesario realizar un estudio, por un lado, de las funcionalidades que ofrece el Campus Virtual, y por otro, de cuáles de estas funcionalidades están disponibles en los recursos para.TI@UCM. El siguiente paso sería plantear cómo se pueden implementar las funcionalidades buscadas y no encontradas en para.TI@UCM usando como base las aplicaciones de Google. Item Dynamic power budget redistribution under a power cap on multi-application environments(Sustainable Computing-Informatics & Systems, 2023) Costero Valero, Luis María; Igual Peña, Francisco Daniel; Olcoz Herrero, KatzalinWe present a two-level implementation of an infrastructure that allows performance maximization under a power-cap on multi-application environments with minimal user intervention. At the application level, we integrate bar (Power Budget-Aware Runtime Scheduler) into existing task-based runtimes, e.g. OpenMP; bar implements combined software/hardware techniques (thread malleability and DVFS) to maximize the application performance without violating a granted power budget. At a higher level, we introduce barman (Power Budget-Aware Resource Manager), a system-wide software able to manage resources globally, gathering power needs of registered applications, and redistributing the available overall power budget across them. The combination and co-operative operation of both pieces of software yields performance and energy efficiency improvements on environments in which power capping is established globally, and also granted asymmetrically to different co-existing applications. This behaviour is demonstrated to be stable under different workloads (a selection of task-based scientific applications and PARSEC benchmarks are tested) and different levels of power capping.Item Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding(The Journal of Supercomputing, 2020) Costero Valero, Luis María; Igual Peña, Francisco Daniel; Olcoz Herrero, Katzalin; Tirado Fernández, José FranciscoThe coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building and serving the most appropriate set of actions (application control knobs and system resource assignment) to concurrent applications in an automatic and optimal fashion become mandatory. In this paper, we propose strategies to build and serve this type of knowledge to concurrent applications by leveraging Reinforcement Learning techniques. Taking multi-user video transcoding as a driving example, our experimental results reveal an excellent adaptation of resource and knob management to heterogeneous QoS requests, and increases in the amount of concurrently served users up to 1.24× compared with alternative approaches considering homogeneous QoS requests.Item Acceleration and energy consumption optimization in cascading classifiers for face detection on low-cost ARM big. LITTLE asymmetric architectures(International Journal of Circuit Theory and Applications, 2018) Corpas, Alberto; Costero Valero, Luis María; Botella Juan, Guillermo; Igual Peña, Francisco Daniel; García, Carlos; Rodríguez, ManuelThis paper proposes a mechanism to accelerate and optimize the energy consumption of a face detection software based on Haar-like cascading classifiers, taking advantage of the features of low-cost asymmetric multicore processors (AMPs) with limited power budget. A modelling and task scheduling/allocation is proposed in order to efficiently make use of the existing features on big. LITTLE ARM processors, including (1) source-code adaptation for parallel computing, which enables code acceleration by applying the OmpSs programming model, a task-based programming model that handles data-dependencies between tasks in a transparent fashion; (2) different OmpSs task allocation policies which take into account the processor asymmetry and can dynamically set processing resources in a more efficient way based on their particular features. The proposed mechanism can be efficiently applied to take advantage of the processing elements existing on low-cost and low-energy multi-core embedded devices executing object detection algorithms based on cascading classifiers. Although these classifiers yield the best results for detection algorithms in the field of computer vision, their high computational requirements prevent them from being used on these devices under real-time requirements. Finally, we compare the energy efficiency of a heterogeneous architecture based on AMPs with a suitable task scheduling with that of a homogeneous symmetric architecture.Item Fine‐grain task‐parallel algorithms for matrix factorizations and inversion on many‐threaded CPUs(Concurrency and Computation: Practice and Experience, 2022) Catalán Pallarés, Sandra; Herrero, José R.; Igual Peña, Francisco Daniel; Quintana‐Ortí, Enrique S.; Rodríguez Sánchez, Rafael- Project number: 38
Item Metodología de internacionalización de material docente basada en el uso de Markdown y Pandoc(2018) Sáez Alcaide, Juan Carlos; Sánchez-Elez Martín, Marcos; Risco Martín, José Luis; Castro Rodríguez, Fernando; Prieto Matías, Manuel; Sáez Puche, Regino; Chaver Martínez, Daniel Ángel; Olcoz Herrero, Katzalin; Clemente Barreira, Juan Antonio; Igual Peña, Francisco Daniel; García García, Adrián; Sánchez Foces, DavidLa internacionalización de la docencia ofrece grandes oportunidades para la Universidad, pero también plantea retos significativos para estudiantes y profesores. En particular, la creación y mantenimiento efectivo del material docente de una asignatura impartida simultáneamente en varios idiomas y con alto grado de coordinación entre los distintos grupos de la misma (p.ej., examen final/prácticas comunes para todos los estudiantes) puede suponer un importante desafío para los profesores. Para hacer frente a este problema, hemos diseñado una estrategia específica para la creación y gestión de material docente en dual (p.ej., inglés-español), y desarrollado un conjunto de herramientas multiplataforma para ponerla en práctica. La idea general es mantener en un mismo fichero de texto el contenido del documento que se desee construir en ambos idiomas, proporcionando justo detrás de cada párrafo y título en uno de los idiomas su traducción al otro idioma, empleando delimitadores especiales. Para crear estos documentos duales se emplea Markdown, un lenguaje de marcado ligero, que dada su sencillez y versatilidad está teniendo una rápida adopción por un amplio espectro de profesionales: desde escritores de novelas o periodistas, hasta administradores de sitios web. A partir de los documentos duales creados con Markdown, es posible generar automáticamente el documento final para cada idioma en el formato deseado que se pondrá a disposición de los estudiantes. Para esta tarea, nos basamos en el uso de la herramienta Pandoc, que permite realizar la conversión de documentos Markdown a una gran cantidad de formatos, como PDF, docx (Microsoft Word), EPUB (libro electrónico) o HTML. Como parte de nuestro proyecto, hemos creado extensiones de Pandoc para permitir la creación de documentos duales en Markdown y para aumentar la expresividad de este lenguaje con construcciones comunmente utilizadas en documentos de carácter docente. Item MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video Transcoding(2019) Costero Valero, Luis María; Iranfar, Arman; Zapater Sancho, Marina; Igual Peña, Francisco Daniel; Olcoz Herrero, Katzalin; Atienza Alonso, DavidReal-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers’ infrastructures in current multi-user environments. High Efficiency Video Coding (HEVC) makes efficient online transcoding feasible as it enhances user experience by providing the adequate video configuration, reduces pressure on the network, and minimizes inefficient and costly video storage. However, the computational complexity of HEVC, together with its myriad of configuration parameters, raises challenges for power management, throughput control, and Quality of Service (QoS) satisfaction. This is particularly challenging in multi-user environments where multiple users with different resolution demands and bandwidth constraints need to be served simultaneously. In this work, we present MAMUT, a multiagent machine learning approach to tackle these challenges. Our proposal breaks the design space composed of run-time adaptation of the transcoder and system parameters into smaller sub-spaces that can be explored in a reasonable time by individual agents. While working cooperatively, each agent is in charge of learning and applying the optimal values for internal HEVC and system-wide parameters. In particular, MAMUT dynamically tunes Quantization Parameter, selects number of threads per video, and sets the operating frequency with throughput and video quality objectives under compression and power consumption constraints. We implement MAMUT on an enterprise multicore server and compare equivalent scenarios to state-ofthe-art alternative approaches. The obtained results reveal that MAMUT consistently attains up to 8x improvement in terms of FPS violations (and thus Quality of Service), 24% power reduction, as well as faster and more accurate adaptation both to the video contents and available resources.Item Applying game-learning environments to power capping scenarios via reinforcement learning(Cloud Computing, Big Data and Emerging Topics, 2022) Hernández Aguado, Pablo; Costero Valero, Luis María; Olcoz Herrero, Katzalin; Igual Peña, Francisco DanielResearch in deep learning for video game playing has received much attention and provided very relevant results in the last years. Frameworks and libraries have been developed to ease game playing research leveraging Reinforcement Learning techniques. In this paper, we propose to use two of them (RLLIB and GYM) in a very different scenario, such as learning to apply resource management policies in a multi-core server, specifically, we leverage the facilities of both frameworks coupled to derive policies for power-capping. Using RLlib and Gym enables implementing different resource management policies in a simple and fast way and, as they are based on neural networks, guarantees the efficiency in the solution, and the use of hardware accelerators for both training and inference. The results demonstrate that game-learning environments provide an effective support to cast a completely different scenario, and open new research avenues in the field of resource management using reinforcement learning techniques with minimal development effort.