514 research outputs found

    Large-scale grid-enabled lattice-Boltzmann simulations of complex fluid flow in porous media and under shear

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    Well designed lattice-Boltzmann codes exploit the essentially embarrassingly parallel features of the algorithm and so can be run with considerable efficiency on modern supercomputers. Such scalable codes permit us to simulate the behaviour of increasingly large quantities of complex condensed matter systems. In the present paper, we present some preliminary results on the large scale three-dimensional lattice-Boltzmann simulation of binary immiscible fluid flows through a porous medium derived from digitised x-ray microtomographic data of Bentheimer sandstone, and from the study of the same fluids under shear. Simulations on such scales can benefit considerably from the use of computational steering and we describe our implementation of steering within the lattice-Boltzmann code, called LB3D, making use of the RealityGrid steering library. Our large scale simulations benefit from the new concept of capability computing, designed to prioritise the execution of big jobs on major supercomputing resources. The advent of persistent computational grids promises to provide an optimal environment in which to deploy these mesoscale simulation methods, which can exploit the distributed nature of compute, visualisation and storage resources to reach scientific results rapidly; we discuss our work on the grid-enablement of lattice-Boltzmann methods in this context.Comment: 17 pages, 6 figures, accepted for publication in Phil.Trans.R.Soc.Lond.

    Cooperative high-performance storage in the accelerated strategic computing initiative

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    The use and acceptance of new high-performance, parallel computing platforms will be impeded by the absence of an infrastructure capable of supporting orders-of-magnitude improvement in hierarchical storage and high-speed I/O (Input/Output). The distribution of these high-performance platforms and supporting infrastructures across a wide-area network further compounds this problem. We describe an architectural design and phased implementation plan for a distributed, Cooperative Storage Environment (CSE) to achieve the necessary performance, user transparency, site autonomy, communication, and security features needed to support the Accelerated Strategic Computing Initiative (ASCI). ASCI is a Department of Energy (DOE) program attempting to apply terascale platforms and Problem-Solving Environments (PSEs) toward real-world computational modeling and simulation problems. The ASCI mission must be carried out through a unified, multilaboratory effort, and will require highly secure, efficient access to vast amounts of data. The CSE provides a logically simple, geographically distributed, storage infrastructure of semi-autonomous cooperating sites to meet the strategic ASCI PSE goal of highperformance data storage and access at the user desktop

    Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey

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    The Next Generation Virgo Cluster Survey is a 104 square degree survey of the Virgo Cluster, carried out using the MegaPrime camera of the Canada-France-Hawaii telescope, from semesters 2009A-2012A. The survey will provide coverage of this nearby dense environment in the universe to unprecedented depth, providing profound insights into galaxy formation and evolution, including definitive measurements of the properties of galaxies in a dense environment in the local universe, such as the luminosity function. The limiting magnitude of the survey is g_AB = 25.7 (10 sigma point source), and the 2 sigma surface brightness limit is g_AB ~ 29 mag arcsec^-2. The data volume of the survey (approximately 50 terabytes of images), while large by contemporary astronomical standards, is not intractable. This renders the survey amenable to the methods of astroinformatics. The enormous dynamic range of objects, from the giant elliptical galaxy M87 at M(B) = -21.6, to the faintest dwarf ellipticals at M(B) ~ -6, combined with photometry in 5 broad bands (u* g' r' i' z'), and unprecedented depth revealing many previously unseen structures, creates new challenges in object detection and classification. We present results from ongoing work on the survey, including photometric redshifts, Virgo cluster membership, and the implementation of fast data mining algorithms on the infrastructure of the Canadian Astronomy Data Centre, as part of the Canadian Advanced Network for Astronomical Research (CANFAR).Comment: 8 pages, 2 figures. Accepted for the Joint Workshop and Summer School: Astrostatistics and Data Mining in Large Astronomical Databases, La Palma, May 30th - June 3rd 2011. A higher resolution version is available at http://sites.google.com/site/nickballastronomer/publication

    A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems

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    Heterogeneous systems are becoming more common on High Performance Computing (HPC) systems. Even using tools like CUDA and OpenCL it is a non-trivial task to obtain optimal performance on the GPU. Approaches to simplifying this task include Merge (a library based framework for heterogeneous multi-core systems), Zippy (a framework for parallel execution of codes on multiple GPUs), BSGP (a new programming language for general purpose computation on the GPU) and CUDA-lite (an enhancement to CUDA that transforms code based on annotations). In addition, efforts are underway to improve compiler tools for automatic parallelization and optimization of affine loop nests for GPUs and for automatic translation of OpenMP parallelized codes to CUDA. In this paper we present an alternative approach: a new computational framework for the development of massively data parallel scientific codes applications suitable for use on such petascale/exascale hybrid systems built upon the highly scalable Cactus framework. As the first non-trivial demonstration of its usefulness, we successfully developed a new 3D CFD code that achieves improved performance.Comment: Parallel Computing 2011 (ParCo2011), 30 August -- 2 September 2011, Ghent, Belgiu

    Concept-driven visualization for terascale data analytics

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    Over the past couple of decades the amount of scientific data sets has exploded. The science community has since been facing the common problem of being drowned in data, and yet starved of information. Identification and extraction of meaningful features from large data sets has become one of the central problems of scientific research, for both simulation as well as sensory data sets. The problems at hand are multifold and need to be addressed concurrently to provide scientists with the necessary tools, methods, and systems. Firstly, the underlying data structures and management need to be optimized for the kind of data most commonly used in scientific research, i.e. terascale time-varying, multi-dimensional, multi-variate, and potentially non-uniform grids. This implies avoidance of data duplication, utilization of a transparent query structure, and use of sophisticated underlying data structures and algorithms.Secondly, in the case of scientific data sets, simplistic queries are not a sufficient method to describe subsets or features. For time-varying data sets, many features can generally be described as local events, i.e. spatially and temporally limited regions with characteristic properties in value space. While most often scientists know quite well what they are looking for in a data set, at times they cannot formally or definitively describe their concept well to computer science experts, especially when based on partially substantiated knowledge. Scientists need to be enabled to query and extract such features or events directly and without having to rewrite their hypothesis into an inadequately simple query language. Thirdly, tools to analyze the quality and sensitivity of these event queries itself are required. Understanding local data sensitivity is a necessity for enabling scientists to refine query parameters as needed to produce more meaningful findings.Query sensitivity analysis can also be utilized to establish trends for event-driven queries, i.e. how does the query sensitivity differ between locations and over a series of data sets. In this dissertation, we present an approach to apply these interdependent measures to aid scientists in better understanding their data sets. An integrated system containing all of the above tools and system parts is presented

    Matrix-free weighted quadrature for a computationally efficient isogeometric kk-method

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    The kk-method is the isogeometric method based on splines (or NURBS, etc.) with maximum regularity. When implemented following the paradigms of classical finite element methods, the computational resources required by the kk-method are prohibitive even for moderate degree. In order to address this issue, we propose a matrix-free strategy combined with weighted quadrature, which is an ad-hoc strategy to compute the integrals of the Galerkin system. Matrix-free weighted quadrature (MF-WQ) speeds up matrix operations, and, perhaps even more important, greatly reduces memory consumption. Our strategy also requires an efficient preconditioner for the linear system iterative solver. In this work we deal with an elliptic model problem, and adopt a preconditioner based on the Fast Diagonalization method, an old idea to solve Sylvester-like equations. Our numerical tests show that the isogeometric solver based on MF-WQ is faster than standard approaches (where the main cost is the matrix formation by standard Gaussian quadrature) even for low degree. But the main achievement is that, with MF-WQ, the kk-method gets orders of magnitude faster by increasing the degree, given a target accuracy. Therefore, we are able to show the superiority, in terms of computational efficiency, of the high-degree kk-method with respect to low-degree isogeometric discretizations. What we present here is applicable to more complex and realistic differential problems, but its effectiveness will depend on the preconditioner stage, which is as always problem-dependent. This situation is typical of modern high-order methods: the overall performance is mainly related to the quality of the preconditioner
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