GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering

Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures, and does not scale well with the size of the models. We present a GPU-based progressive mesh simplification approach which enables the interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we develop a novel data structure to represent the progressive LOD mesh, and design a parallel mesh simplification algorithm towards GPU architecture. Second, we propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel mesh simplification algorithm and GPU-based streaming approach significantly improve the overall rendering performance

MARCOS, a numerical tool for the simulation of multiple time-dependent non-linear diffusive shock acceleration

We present a new code aimed at the simulation of diffusive shock acceleration (DSA), and discuss various test cases which demonstrate its ability to study DSA in its full time-dependent and non-linear developments. We present the numerical methods implemented, coupling the hydrodynamical evolution of a parallel shock (in one space dimension) and the kinetic transport of the cosmic-rays (CR) distribution function (in one momentum dimension), as first done by Falle. Following Kang and Jones and collaborators, we show how the adaptive mesh refinement technique (AMR) greatly helps accommodating the extremely demanding numerical resolution requirements of realistic (Bohm-like) CR diffusion coefficients. We also present the paral lelization of the code, which allows us to run many successive shocks at the cost of a single shock, and thus to present the first direct numerical simulations of linear and non-linear multiple DSA, a mechanism of interest in various astrophysical environments such as superbubbles, galaxy clusters and early cosmological flows.Comment: accepted for publication in MNRAS by the Royal Astronomical Society and Blackwell Publishin

Massive and refined: a sample of large galaxy clusters simulated at high resolution. I:Thermal gas and shock waves properties

We present a sample of 20 massive galaxy clusters with total virial masses in the range of 6 10^14 M_sol<M(vir)< 2 10^15M_sol, re-simulated with a customized version of the 1.5. ENZO code employing Adaptive Mesh Refinement. This technique allowed us to obtain unprecedented high spatial resolution (25kpc/h) up to the distance of 3 virial radii from the clusters center, and makes it possible to focus with the same level of detail on the physical properties of the innermost and of the outermost cluster regions, providing new clues on the role of shock waves and turbulent motions in the ICM, across a wide range of scales. In this paper, a first exploratory study of this data set is presented. We report on the thermal properties of galaxy clusters at z=0. Integrated and morphological properties of gas density, gas temperature, gas entropy and baryon fraction distributions are discussed, and compared with existing outcomes both from the observational and from the numerical literature. Our cluster sample shows an overall good consistency with the results obtained adopting other numerical techniques (e.g. Smoothed Particles Hydrodynamics), yet it provides a more accurate representation of the accretion patterns far outside the cluster cores. We also reconstruct the properties of shock waves within the sample by means of a velocity-based approach, and we study Mach numbers and energy distributions for the various dynamical states in clusters, giving estimates for the injection of Cosmic Rays particles at shocks. The present sample is rather unique in the panorama of cosmological simulations of massive galaxy clusters, due to its dynamical range, statistics of objects and number of time outputs. For this reason, we deploy a public repository of the available data, accessible via web portal at http://data.cineca.it.Comment: 26 pages, 20 figures, New Astronomy accepted. Reference list updated. Higher quality versions of the paper can be found at: http://www.ira.inaf.it/~vazza/papers A public archive of galaxy clusters data is accessible at http://data.cineca.it

Extension of the Finite Integration Technique including dynamic mesh refinement and its application to self-consistent beam dynamics simulations

An extension of the framework of the Finite Integration Technique (FIT) including dynamic and adaptive mesh refinement is presented. After recalling the standard formulation of the FIT, the proposed mesh adaptation procedure is described. Besides the linear interpolation approach, a novel interpolation technique based on specialized spline functions for approximating the discrete electromagnetic field solution during mesh adaptation is introduced. The standard FIT on a fixed mesh and the new adaptive approach are applied to a simulation test case with known analytical solution. The numerical accuracy of the two methods are shown to be comparable. The dynamic mesh approach is, however, much more efficient. This is also demonstrated for the full scale modeling of the complete RF gun at the Photo Injector Test Facility DESY Zeuthen (PITZ) on a single computer. Results of a detailed design study addressing the effects of individual components of the gun onto the beam emittance using a fully self-consistent approach are presented.Comment: 33 pages, 14 figures, 4 table

MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics