Examinando por Materia "methods"

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    Dynamic server allocation in virtual environments, using quadtrees for dynamic space partition
    (2003-01-01) Restrepo, A.; Montoya, A.; Trefftz, H.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    In actual applications of virtual environments, one of the problems that has received attention is the way to assign computational resources to those who participate in the system. In most cases, static resource allocation is used. Static allocation is simple but has several limitations, among them, the missutilization of resources. In this paper we present a proof-of-concept of a new system and report the results of experiments with a system that accomplishes dynamic resource allocation by using Quadtrees for space partitioning, in a Shared Virtual Environment.
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    An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
    (SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y Conservación
    Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.
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    Solving large systems of linear equations on GPUs
    (Springer Verlag, 2018-01-01) Llano-Ríos T.F.; Ocampo-García J.D.; Yepes-Ríos J.S.; Correa-Zabala F.J.; Trefftz C.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    Graphical Processing Units (GPUs) have become more accessible peripheral devices with great computing capacity. Moreover, GPUs can be used not only to accelerate the graphics produced by a computer but also for general purpose computing. Many researchers use this technique on their personal workstations to accelerate the execution of their programs and have often encountered that the amount of memory available on GPU cards is typically smaller than the amount of memory available on the host computer. We are interested in exploring approaches to solve problems with this restriction. Our main contribution is to devise ways in which portions of the problem can be moved to the memory of the GPU to be solved using its multiprocessing capabilities. We implemented on a GPU the Jacobi iterative method to solve systems of linear equations and report the details from the results obtained, analyzing its performance and accuracy. Our code solves a system of linear equations large enough to exceed the card’s memory, but not the host memory. Significant speedups were observed, as the execution time taken to solve each system is faster than those obtained with Intel® MKL and Eigen, libraries designed to work on CPUs. © Springer Nature Switzerland AG 2018.