1,038 research outputs found

    Accelerating scientific codes by performance and accuracy modeling

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    Scientific software is often driven by multiple parameters that affect both accuracy and performance. Since finding the optimal configuration of these parameters is a highly complex task, it extremely common that the software is used suboptimally. In a typical scenario, accuracy requirements are imposed, and attained through suboptimal performance. In this paper, we present a methodology for the automatic selection of parameters for simulation codes, and a corresponding prototype tool. To be amenable to our methodology, the target code must expose the parameters affecting accuracy and performance, and there must be formulas available for error bounds and computational complexity of the underlying methods. As a case study, we consider the particle-particle particle-mesh method (PPPM) from the LAMMPS suite for molecular dynamics, and use our tool to identify configurations of the input parameters that achieve a given accuracy in the shortest execution time. When compared with the configurations suggested by expert users, the parameters selected by our tool yield reductions in the time-to-solution ranging between 10% and 60%. In other words, for the typical scenario where a fixed number of core-hours are granted and simulations of a fixed number of timesteps are to be run, usage of our tool may allow up to twice as many simulations. While we develop our ideas using LAMMPS as computational framework and use the PPPM method for dispersion as case study, the methodology is general and valid for a range of software tools and methods

    Nanostructure Modeling in Oxide Ceramics Using Large Scale Parallel Molecular Dynamics Simulations.

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    The purpose of this dissertation is to investigate the properties and processes in nanostructured oxide ceramics using molecular-dynamics (MD) simulations. These simulations are based on realistic interatomic potentials and require scalable and portable multiresolution algorithms implemented on parallel computers. The dynamics of oxidation of aluminum nanoclusters is studied with a MD scheme that can simultaneously treat metallic and oxide systems. Dynamic charge transfer between anions and cations which gives rise to a compute-intensive Coulomb interaction, is treated by the O(N) Fast Multipole Method. Structural and dynamical correlations and local stresses reveal significant charge transfer and stress variations which cause rapid diffusion of Al and O on the nanocluster surface. At a constant temperature, the formation of an amorphous surface-oxide layer is observed during the first 100 picoseconds. Subsequent sharp decrease in O diffusion normal to the cluster surface arrests the growth of the oxide layer with a saturation thickness of 4 nanometers; this is in excellent agreement with experiments. Analyses of the oxide scale reveal significant charge transfer and variations in local structure. When the heat is not extracted from the cluster, the oxidizing reaction becomes explosive. Sintering, structural correlations, vibrational properties, and mechanical behavior of nanophase silica glasses are also studied using the MD approach based on an empirical interatomic potential that consists of both two and three-body interactions. Nanophase silica glasses with densities ranging from 76 to 93% of the bulk glass density are obtained using an isothermal-isobaric MD approach. During the sintering process, the pore sizes and distribution change without any discernable change in the pore morphology. The height and position of the first sharp diffraction peak (the signature of intermediate-range order) in the neutron static structure factor shows significant differences in the nanophase glasses relative to the bulk silica glass. Enhancement of the low-energy vibrational modes is observed. The effect of densification on mechanical properties is also examined

    Parallelization solutions for the YNANO Discontinua Simulations.

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    PhDIn the context of constant and fast progresses in nano technology, discontinua based computation simulations are becoming increasingly important, especially in the context of virtual experimentations. The efficiency of discontinua based nanoscale simulations are still limited by CPU capacity (the number of simulation particles in the system). It is accepted that parallelization will play an important role in solving this problem. In this thesis, two parallelization approaches have been undertaken to parallelize the YNANO discontinua simulations. The scope of the work includes parallelization of the YNANO using the shared-memory approach OpenMP and the distributed-memory approach MPI, and also includes a novel MR_PB linear contact detection algorithm which can be used under periodic boundary conditions. The developed MPI parallelization solutions are compatible with the MR linear contact detection algorithm used in the sequential YNANO, the developed solutions preserves the linearity of both MR_Sort and MR_Search algorithm. The overall performance and scalability of the parallelization has been studied using nanoscale simulations in fluid dynamics and aerodynamics

    Structure and Dynamics of Poly(methyl-methacrylate)/Graphene systems through Atomistic Molecular Dynamics Simulations

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    The main goal of the present work is to examine the effect of graphene layers on the sructural and dynamical properties of polymer systems. We study hybrid poly(methyl methacrylate) (PMMA)/graphene interfacial systems, through detailed atomistic molecular dynamics (MD) simulations. In order to characterize the interface, various properties related to density, structure and dynamics of polymer chains are calculated, as a function of the distance from the substrate. A series of different hybrid systems, with width ranging between [2.60 – 13.35] nm, are being modeled. In addition, we compare the properties of the macromolecular chains to the properties of the orresponding bulk system at the same temperature. We observe a strong effect of graphene layers on both structure and dynamics of the PMMA chains. Furthermore the PMMA/graphene interface is characterized by different length scales, depending on the actual property we probe: Density of PMMA polymer chains is larger than the bulk value, for polymer chains close to graphene layers up to distances of about [1.0-1.5]nm. Chain conformations are perturbed for distances up to about 2-3 radius of gyration from graphene. Segmental dynamics of PMMA is much slower close to the solid layers up to about [2-3]nm. Finally terminal-chain dynamics is slower, compared to the bulk one, up to distances of about 5-7 radius of gyration

    Structure and dynamics of water at carbon-based interfaces

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    Water structure and dynamics are affected by the presence of a nearby interface. Here, first we review recent results by molecular dynamics simulations about the effect of different carbon-based materials, including armchair carbon nanotubes and a variety of graphene sheets—flat and with corrugation—on water structure and dynamics. We discuss the calculations of binding energies, hydrogen bond distributions, water’s diffusion coefficients and their relation with surface’s geometries at different thermodynamical conditions. Next, we present new results of the crystallization and dynamics of water in a rigid graphene sieve. In particular, we show that the diffusion of water confined between parallel walls depends on the plate distance in a non-monotonic way and is related to the water structuring, crystallization, re-melting and evaporation for decreasing inter-plate distance. Our results could be relevant in those applications where water is in contact with nanostructured carbon materials at ambient or cryogenic temperatures, as in man-made superhydrophobic materials or filtration membranes, or in techniques that take advantage of hydrated graphene interfaces, as in aqueous electron cryomicroscopy for the analysis of proteins adsorbed on graphene.Postprint (author's final draft

    Characterization, modeling, and simulation of multiscale directed-assembly systems

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    Nanoscience is a rapidly developing field at the nexus of all physical sciences which holds the potential for mankind to gain a new level of control of matter over matter and energy altogether. Directed-assembly is an emerging field within nanoscience in which non-equilibrium system dynamics are controlled to produce scalable, arbitrarily complex and interconnected multi-layered structures with custom chemical, biologically or environmentally-responsive, electronic, or optical properties. We construct mathematical models and interpret data from direct-assembly experiments via application and augmentation of classical and contemporary physics, biology, and chemistry methods. Crystal growth, protein pathway mapping, LASER tweezers optical trapping, and colloid processing are areas of directed-assembly with established experimental techniques. We apply a custom set of characterization, modeling, and simulation techniques to experiments to each of these four areas. Many of these techniques can be applied across several experimental areas within directed-assembly and to systems featuring multiscale system dynamics in general. We pay special attention to mathematical methods for bridging models of system dynamics across scale regimes, as they are particularly applicable and relevant to directed-assembly. We employ massively parallel simulations, enabled by custom software, to establish underlying system dynamics and develop new device production methods

    Scalable parallel molecular dynamics algorithms for organic systems

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    A scalable parallel algorithm, Macro-Molecular Dynamics (MMD), has been developed for large-scale molecular dynamics simulations of organic macromolecules, based on space-time multi-resolution techniques and dynamic management of distributed lists. The algorithm also includes the calculation of long range forces using Fast Multipole Method (FMM). FMM is based on the octree data structure, in which each parent cell is divided into 8 child cells and this division continues until the cell size is equal to the non-bonded interaction cutoff length. Due to constant number of operations performed at each stage of the octree, the FMM algorithm scales as O(N). Design and analysis of MMD and FMM algorithms are presented. Scalability tests are performed on three tera-flop machines: 1024-processor Intel Xeon-based Linux cluster, SuperMike at LSU, 1184-processor IBM SP4 Marcellus and the 512-processor Compaq AlphaServer Emerald at the U.S. Army Engineer Research and Development Center (ERDC) MSRC. The tests show that the Linux cluster outperforms the SP4 for the MMD application. The tests also show significant effects of memory- and cache-sharing on the performance
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