Examinando por Materia "Computing"

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    Simulating Soft Tissues using a GPU approach of the Mass-Spring Model
    (IEEE COMPUTER SOC, 2010-01-01) Diaz Leon, Christian Andres; Eliuk, Steven; Trefftz Gomez, Helmuth; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    The recent advances in the fields such as modeling bio-mechanics of living tissues, haptic technologies, computational capacity, and graphics realism have created conditions necessary in order to develop effective surgical training using virtual environments. However, virtual simulators need to meet two requirements, they need to be real-time and highly realistic. The most expensive computational task in a surgical simulator is that of the physical model. The physical model is the component responsible to simulate the deformation of the anatomical structures and the most important factor in order to obtain realism. In this paper we present a novel approach to virtual surgery. The novelty comes in two forms: specifically a highly realistic mass-spring model, and a GPU based technique, and analysis, that provides a nearly 80x speedup over serial execution and 20x speedup over CPU based parallel execution.
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    Simulating soft tissues using a GPU approach of the mass-spring model
    (2010-01-01) Leon, C.A.D.; Eliuk, S.; Gomez, H.T.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    The recent advances in the fields such as modeling bio-mechanics of living tissues, haptic technologies, computational capacity, and graphics realism have created conditions necessary in order to develop effective surgical training using virtual environments. However, virtual simulators need to meet two requirements, they need to be real-time and highly realistic. The most expensive computational task in a surgical simulator is that of the physical model. The physical model is the component responsible to simulate the deformation of the anatomical structures and the most important factor in order to obtain realism. In this paper we present a novel approach to virtual surgery. The novelty comes in two forms: specifically a highly realistic mass-spring model, and a GPU based technique, and analysis, that provides a nearly 80x speedup over serial execution and 20x speedup over CPU based parallel execution. ©2010 IEEE.
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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.