Person: Gómez Pérez, José Ignacio
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First Name
José Ignacio
Last Name
Gómez Pérez
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
Identifiers
4 results
Search Results
Now showing 1 - 4 of 4
Item Improving the representativeness of simulation intervals for the cache memory system(IEEE Access, 2024) Bueno Mora, Nicolás; Castro Rodríguez, Fernando; Piñuel Moreno, Luis; Gómez Pérez, José Ignacio; Catthor, FranckyAccurate simulation techniques are indispensable to efficiently propose new memory or architectural organizations. As implementing new hardware concepts in real systems is often not feasible, cycle-accurate simulators employed together with certain benchmarks are commonly used. However, detailed simulators may take too much time to execute these programs until completion. Therefore, several techniques aimed at reducing this time are usually employed. These schemes select fragments of the source code considered as representative of the entire application’s behaviour–mainly in terms of performance, but not plenty considering the behaviour of cache memory levels–and only these intervals are simulated. Our hypothesis is that the different simulation windows currently employed when evaluating microarchitectural proposals, especially those involving the last level cache (LLC), do not reproduce the overall cache behaviour during the entire execution, potentially leading to wrong conclusions on the real performance of the proposals assessed. In this work, we first demonstrate this hypothesis by evaluating different cache replacement policies using various typical simulation approaches. Consequently, we also propose a simulation strategy, based on the applications’ LLC activity, which mimics the overall behaviour of the cache much closer than conventional simulation intervals. Our proposal allows a fairer comparison between cache-related approaches as it reports, on average, a number of changes in the relative order among the policies assessed – with respect to the full simulation – more than 30% lower than that of conventional strategies, maintaining the simulation time largely unchanged and without losing accuracy on performance terms, especially for memory-intensive applications.Item Funcionamiento de la herramienta OpenIRS-UCM y sus sinergias con Moodle(VII Jornada Campus Virtual UCM: valorar, validar y difundir Campus Virtual, 2012) García Sánchez, Carlos; Castro Rodríguez, Fernando; Chaver Martínez, Daniel Ángel; Tenllado Van Der Reijden, Christian Tomás; Gómez Pérez, José Ignacio; López Orozco, José Antonio; Piñuel Moreno, LuisLos sistemas de respuesta interactiva han ido ganando aceptación dentro de la comunidad educativa en los últimos años y una prueba clara de ello es el número creciente de los sistemas comerciales disponibles hoy en el mercado. Sin embargo, la mayoría de las soluciones se basan en sistemas que están cerrados, son rígidos y dependientes del software instalado en el computador del profesor. Presentamos en este trabajo una nueva herramienta gratuita que hemos denominado OpenIRS-UCM que incorpora la mayoría de las funcionalidades de las aplicaciones comerciales con la ventaja de integrar varios tipos de mandos comerciales con otros dispositivos como smartphones, PDAs, portátiles, etc. Además, permite interactuar con la plataforma del campus virtual de Moodle incrementando exponencialmente sus posibilidades de uso.Item Spatio-temporal resolution of irradiance samples in machine learning approaches for irradiance forecasting(IEEE access, 2020) Eschenbach, Annette; Yepes, Guillermo; Tenllado Van Der Reijden, Christian Tomás; Gómez Pérez, José Ignacio; Piñuel Moreno, Luis; Zarzalejo, Luiis F.; Wilbert, StefanImproving short term solar irradiance forecasting is crucial to increase the market share of the solar energy production. This paper analyzes the impact of using spatially distributed irradiance sensors as inputs to four machine learning algorithms: ARX, NN, RRF and RT. We used data from two different sensor networks for our experiments, the NREL dataset that includes data from 17 sensors that cover a 1 km^2 area and the InfoRiego dataset which includes data from 50 sensors that cover an area of 94 Km^2. Several studies have been published that use these datasets individually, to the author knowledge this is the flrst work that evaluates the influence of the spatially distributed data across a range from 0.5 to 17 sensors per km^2. We show that all of algorithms evaluated are able to take advantage of the data from the surroundings, from the very short forecast horizons of 10s up to a few hours, and that the wind direction and intensity plays an important role in the optimal distribution of the network and its density. We show that these machine learning methods are more effective on the short horizons when data is obtained from a dense enough network to capture the cloud movements in the prediction interval, and that in those cases complex non-linear models give better results. On the other hand, if only a sparse network is available, the simpler linear models give better results. The skills obtained with the models under test range from 13% to 70%, depending on the sensor network density, time resolution and lead time.- Project number: 346
Item Generación automática de informes del programa Docentia para las memorias de seguimiento de los centros(2015) López Orozco, José Antonio; Díaz Agudo, María Belén; Piñuel Moreno, Luis; Chaver Martínez, Daniel Ángel; Gómez Pérez, José Ignacio; Castro Rodríguez, Fernándo; García Sánchez, Carlos; Tenllado Van Der Reijden, Christian TomásInforme final del Proyecto de Innovación y Mejora de la Calidad Docente 346 de la convocatoria 2014.