18 research outputs found
High-Performance Modelling and Simulation for Big Data Applications
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas
Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI
XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas
Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI
XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas
Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los dÃas 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI
Algorithms and architectures for MCMC acceleration in FPGAs
Markov Chain Monte Carlo (MCMC) is a family of stochastic algorithms which are used to draw random samples from arbitrary probability distributions. This task is necessary to solve a variety of problems in Bayesian modelling, e.g. prediction and model comparison, making MCMC a fundamental tool in modern statistics. Nevertheless, due to the increasing complexity of Bayesian models, the explosion in the amount of data they need to handle and the computational intensity of many MCMC algorithms, performing MCMC-based inference is often impractical in real applications. This thesis tackles this computational problem by proposing Field Programmable Gate Array (FPGA) architectures for accelerating MCMC and by designing novel MCMC algorithms and optimization methodologies which are tailored for FPGA implementation. The contributions of this work include: 1) An FPGA architecture for the Population-based MCMC algorithm, along with two modified versions of the algorithm which use custom arithmetic precision in large parts of the implementation without introducing error in the output. Mapping the two modified versions to an FPGA allows for more parallel modules to be instantiated in the same chip area. 2) An FPGA architecture for the Particle MCMC algorithm, along with a novel algorithm which combines Particle MCMC and Population-based MCMC to tackle multi-modal distributions. A proposed FPGA architecture for the new algorithm achieves higher datapath utilization than the Particle MCMC architecture. 3) A generic method to optimize the arithmetic precision of any MCMC algorithm that is implemented on FPGAs. The method selects the minimum precision among a given set of precisions, while guaranteeing a user-defined bound on the output error. By applying the above techniques to large-scale Bayesian problems, it is shown that significant speedups (one or two orders of magnitude) are possible compared to state-of-the-art MCMC algorithms implemented on CPUs and GPUs, opening the way for handling complex statistical analyses in the era of ubiquitous, ever-increasing data.Open Acces
How general-purpose can a GPU be?
The use of graphics processing units (GPUs) in general-purpose computation (GPGPU) is a growing field. GPU instruction sets, while implementing a graphics pipeline, draw from a range of single instruction multiple datastream (SIMD) architectures characteristic of the heyday of supercomputers. Yet only one of these SIMD instruction sets has been of application on a wide enough range of problems to survive the era when the full range of supercomputer design variants was being explored: vector instructions. Supercomputers covered a range of exotic designs such as hypercubes and the Connection Machine (Fox, 1989). The latter is likely the source of the snide comment by Cray: it had thousands of relatively low-speed CPUs (Tucker & Robertson, 1988). Since Cray won, why are we not basing our ideas on his designs (Cray Inc., 2004), rather than those of the losers? The Top 500 supercomputer list is dominated by general-purpose CPUs, and nothing like the Connection Machine that headed the list in 1993 still exists
Trading the stock market : hybrid financial analyses and evolutionary computation
Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leÃda el 02-07-2014Esta tesis presenta la implementación de un innovador sistema de comercio automatizado que utiliza tres importantes análisis para determinar lugares y momentos de inversión. Para ello, este trabajo profundiza en sistemas automáticos de comercio y estudia series temporales de precios históricos pertenecientes a empresas que cotizan en el mercado bursátil. Estudiamos y clasifcamos las series temporales mediante el uso de una novedosa metodologÃa basada en compresores de software. Este nuevo enfoque permite un estudio teórico de la formación de precios que demuestra resultados de divergencia entre precios reales de mercado y precios modelados mediante paseos aleatorios, apoyando asà el desarrollo de modelos predictivos basados en el análisis de patrones históricos como los descritos en este documento. Además, esta metodologÃa nos permite estudiar el comportamiento de series temporales de precios históricos en distintos sectores industriales mediante la búsqueda de patrones en empresas pertenecientes al mismo sector. Los resultados muestran agrupaciones que indican tendencias de mercado compartidas y ,por tanto, señalan que la inclusión de un análisis industrial puede reportar ventajas en la toma de decisiones de inversión. Comprobada la factibilidad de un sistema de predicción basado en series temporales y demostrada la existencia de tendencias macroeconómicas en las diferentes industrias, proponemos el desarrollo del sistema completo a través de diferentes etapas. Iterativamente y mediante varias aproximaciones, testeamos y analizamos las piezas que componen el sistema nal. Las primeras fases describen un sistema de comercio automatizado, basado en análisis técnico y fundamental de empresas, que presenta altos rendimientos y reduce el riesgo de pérdidas. El sistema utiliza un motor de optimización guiado por una versión modi cada de un algoritmo genético el la que presentamos operadores innovadores que proporcionan mecanismos para evitar una convergencia prematura del algoritmo y mejorar los resultados de rendimiento nales. Utilizando este mismo sistema de comercio automático proponemos técnicas de optimización novedosas en relación a uno de los problemas más caracterÃsticos de estos sistemas, el tiempo de ejecución. Presentamos la paralelización del sistema de comercio automatizado mediante dos técnicas de computación paralela, computación distribuida y procesamiento grá co. Ambas arquitecturas presentan aceleraciones elevadas alcanzando los x50 y x256 respectivamente. Estápas posteriores presentan un cambio de metodologia de optimización, algoritmos genéticos por evolución gramatical, que nos permite comparar ambas estrategias e implementar caracterÃsticas más avanzadas como reglas más complejas o la auto-generación de nuevos indicadores técnicos. Testearemos, con datos nancieros recientes, varios sistemas de comercio basados en diferentes funciones de aptitud, incluyendo una innovadora versión multi-objetivo, que nos permitirán analizar las ventajas de cada función de aptitud. Finalmente, describimos y testeamos la metodologÃa del sistema de comercio automatizado basado en una doble capa de gramáticas evolutivas y que combina un análisis técnico, fundamental y macroeconómico en un análisis top-down hÃbrido. Los resultados obtenidos muestran rendimientos medios del 30% con muy pocas operaciones de perdidas.This thesis concerns to the implementation of a complex and pioneering automated trading system which uses three critical analysis to determine time-decisions and portfolios for investments. To this end, this work delves into automated trading systems and studies time series of historical prices related to companies listed in stock markets. Time series are studied using a novel methodology based on clusterings by software compressors. This new approach allows a theoretical study of price formation which shows results of divergence between market prices and prices modelled by random walks, thus supporting the implementation of predictive models based on the analysis of historical patterns. Furthermore, this methodology also provides us the tool to study behaviours of time series of historical prices from di erent industrial sectors seeking patterns among companies in the same industry. Results show clusters of companies pointing out market trends among companies developing similar activities, and suggesting a macroeconomic analysis to take advantage of investment decisions. Tested the feasibility of prediction systems based on analyses related to time series of historical prices and tested the existence of macroeconomic trends in the industries, we propose the implementation of a hybrid automated trading system through several stages which iteratively describe and test the components of the nal system. In the early stages, we implement an automated trading system based on technical and fundamental analysis of companies, it presents high returns and reducing losses. The implementation uses a methodology guided by a modi ed version of a genetic algorithm which presents novel genetic operators avoiding the premature convergence and improving nal results. Using the same automated trading system we propose novel optimization techniques related to one of the characteristic problems of these systems: the execution time. We present the parallelisation of the system using two parallel computing techniques, rst using distributed computation and, second, implementing a version for graphics processors. Both architectures achieve high speed-ups, reaching 50x and 256x respectively, thus, they present the necessary speed-ups required by systems analysing huge amount of nancial data. Subsequent stages present a transformation in the methodology, genetic algorithms for grammatical evolution, which allows us to compare the two evolutionary strategies and to implement more advanced features such as more complex rules or the self-generation of new technical indicators. In this context, we describe several automated trading system versions guided by di erent tness functions, including an innovative multi-objective version that we test with recent nancial data analysing the advantages of each tness function. Finally, we describe and test the methodology of an automated trading system based on a double layer of grammatical evolution combining technical, fundamental and macroeconomic analysis on a hybrid topdown analysis. The results show average returns of 30% with low number of negative operations.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu