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@Article{MadeiraRiStRoRoSiCl:2014:StWoDi,
               author = "Madeira, D. and Ricardo, J. and Stalder, Diego H. and Rocha, L. 
                         and Rosa, Reinaldo Roberto and Silveira Filho, O. T. and Clua, 
                         E.",
          affiliation = "Universidade Federal de S{\~a}o Jo{\~a}o del Rei, S{\~a}o 
                         Jo{\~a}o del Rei, Minas Gerais, Brazil; Universidade Federal 
                         Fluminense, Niter{\'o}i, Rio de Janeiro, Brazil and Universidade 
                         Federal Fluminense, Niter{\'o}i, Rio de Janeiro, Brazil and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Universidade Federal de S{\~a}o Jo{\~a}o del Rei, S{\~a}o 
                         Jo{\~a}o del Rei, Minas Gerais, Brazil and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and Universidade Federal Fluminense, 
                         Niter{\'o}i, Rio de Janeiro, Brazil and Universidade Federal 
                         Fluminense, Niter{\'o}i, Rio de Janeiro, Brazil",
                title = "A strategy to workload division for massively particle-particle 
                         N-body simulations on GPUs",
              journal = "Lecture Notes in Computer Science",
                 year = "2014",
               volume = "8584 LNCS",
               number = "PART 6",
                pages = "455--465",
             keywords = "Cosmology, Computational constraints, Computational power, 
                         Deformation potential, Hierarchical strategies, Lagrangian 
                         turbulence, Mass distribution, N-body simulation, Subdivision 
                         methods, Program processors.",
             abstract = "The new programmable graphical processor units (GPUs) are now 
                         often used as high parallel mathematical co-processors, allowing 
                         many computational-intensive problems to be executed in less time. 
                         It became common and convenient to use single GPUs to implement 
                         different kinds of simulations, or to group them in a grid, so the 
                         computational power can be highly increased, while the power 
                         consumption and physical space increase are significantly lower. 
                         The N-body simulation has been successfully ported to the GPUs. 
                         This algorithm is typically applied to gravitational simulations, 
                         among many other physical problems. In cosmology, one alternative 
                         approach seeks to explain the nature of dark matter as a direct 
                         result of the non-linear space-time curvature, due to different 
                         types of deformation potentials. In order to develop a detailed 
                         study of this new approach, our group is developing the COsmic 
                         LAgrangian TUrbulence Simulator (COLATUS-ENVIU). The simulator 
                         uses the direct particle-particle method to calculate the forces 
                         between the particles, so we eliminate the errors included on the 
                         hierarchical strategies. It gave robust initial results, but was 
                         limited to systems with a small amount of particles. However one 
                         limitation on the use of GPUs on N-body simulation to study the 
                         details on the mass distribution, is that these systems must have 
                         millions (or even billions) of particles, making the use of 
                         particle-particle method in one GPU unfeasible due to memory or 
                         time computational constraints. In this present work we propose a 
                         novel and efficient subdivision method to allow the workload 
                         division, allowing a single GPU to be able to solve large 
                         simulations, while allowing to watch over a particle during all 
                         the simulation.  2014 Springer International Publishing.",
                  doi = "10.1007/978-3-319-09153-2_34",
                  url = "http://dx.doi.org/10.1007/978-3-319-09153-2_34",
                 isbn = "9783319091525",
                 issn = "0302-9743",
                label = "scopus 2014-11 MadeiraRiStRoRoSiCl:2014:StWoDi",
             language = "en",
        urlaccessdate = "28 nov. 2020"
}


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