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A graph-theoretic approach for characterization of precipitates from atom probe tomography data

COMPUTATIONAL MATERIALS SCIENCE 2013年 77卷 335-342页

作者： Samudrala, S. Wodo, O. Suram, S. K. Broderick, S. Rajan, K. Ganapathysubramanian, B. Iowa State Univ Dept Mech Engn Ames IA 50011 USA Iowa State Univ Dept Mat Sci & Engn Ames IA USA

Atom Probe Tomography (APT) represents a revolutionary characterization tool that allows direct-space three-dimensional, atomic-scale resolution imaging along with the chemical identities of each detected atom. Quantitative analysis of APT data to perform characterization of precipitates in alloys gives clear insights into the structure-property relationships and helps in achieving the larger goal of materials-by-design. Most techniques currently used to extract precipitate topology and interface information from APT data are efficient;however, they are based on homogenization of the rich point cloud data which is inherently lossy. Furthermore, these methods require a specified, usually heuristic, concentration-level to draw iso-contours in order to extract characteristics of the precipitate topology. These twin issues of homogenization and heuristics are compelling rationale for the development of a robust, scalable, heuristic-free, graph-based framework, which we call Graph methods for Precipitate Topology Characterization (GraPTop). This framework is motivated by the equivalence between a 3D point cloud data of atoms and an undirected, weighted, labeled graph. By considering the 3D point cloud data as an undirected, weighted, labeled graph, we leverage powerful graph-based algorithms to identify the local topology of precipitates without the necessity of any heuristics. Since GraPTop is based on nearly linear-complexity graph-algorithms, it is scalable to extremely large datasets. Furthermore, the performance of this framework is insensitive to the complexity of the geometry or the number of the precipitates in the point cloud data. We showcase this framework by analyzing several regions of interest in a point cloud Al-Mg-Sc (Aluminium-Magnesium-Scandium) specimen APT data and extract several interesting measures describing the precipitate topology like area, volume, and nonconvexity. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Atom probe tomograph Graph theory Point cloud data Topology scalable algorithms Precipitate characterization

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Mining very large databases

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COMPUTER 1999年第8期32卷 38-+页

作者： Ganti, V Gehrke, J Ramakrishnan, R Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

The explosive growth of databases makes the scalability of data-mining techniques increasingly important. The authors describe algorithms that address three classical data mining problems.

关键词： classification clustering data analysis data analysis data mining data mining algorithms data mining techniques input data input database knowledge discovery in databases main memory market basket analysis pattern classification pattern clustering scalable algorithms strategic decision making vast data stores very large data sets very large database mining very large databases

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Video quality-of-service for consumer terminals - A novel system for programmable components

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2003年第4期49卷 1367-1377页

作者： Hentschel, C Bril, RJ Chen, YW Braspenning, R Lan, TH Philips Res Labs Eindhoven Netherlands Philips Res Labs Briarcliff Manor NY USA

Future consumer terminals will be more and more based on programmable platforms instead of only dedicated hardware. Novel 'scalable video algorithm' (SVA) software modules trade off resource usage against quality of the output signal. SVAs together with a strategy manager and a Quality-of-Service Resource Manager (QoS-RM) aim for flexible, robust, and cost-effective media processing in software on programmable architectures. We developed some SVAs including MPEG-2 decoding and image enhancement algorithms and the basic QoS control software to illustrate the power of the overall system. The resource limited hardware is a currently available DSP board with a single dedicated media processor1. The system supports two inputs and several modes for main and picture-in-picture (PiP) window applications, along with background recording.

关键词： consumer terminals scalable algorithms QoS control dynamic resource management

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Perron vector optimization applied to search engines

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APPLIED NUMERICAL MATHEMATICS 2014年 75卷 77-99页

作者： Fercoq, Olivier Univ Edinburgh Sch Math Edinburgh EH8 9YL Midlothian Scotland

In the last years, Google's PageRank optimization problems have been extensively studied. In that case, the ranking is given by the invariant measure of a stochastic matrix. In this paper, we consider the more general situation in which the ranking is determined by the Perron eigenvector of a nonnegative, but not necessarily stochastic, matrix, in order to cover Kleinberg's HITS algorithm. We also give some results for Tomlin's HOTS algorithm. The problem consists then in finding an optimal outlink strategy subject to design constraints and for a given search engine. We study the relaxed versions of these problems, which means that we should accept weighted hyperlinks. We provide an efficient algorithm for the computation of the matrix of partial derivatives of the criterion, that uses the low rank property of this matrix. We give a scalable algorithm that couples gradient and power iterations and gives a local minimum of the Perron vector optimization problem. We prove convergence by considering it as an approximate gradient method. We then show that optimal linkage strategies of HITS and HOTS optimization problems satisfy a threshold property. We report numerical results on fragments of the real web graph for these search engine optimization problems. (C) 2013 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Optimization Perron-Frobenius theory Search engines scalable algorithms

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scalable EXTENDED DYNAMIC MODE DECOMPOSITION USING RANDOM KERNEL APPROXIMATION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2019年第3期41卷 A1482-A1499页

作者： Degennaro, Anthony M. Urban, Nathan M. Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA Los Alamos Natl Lab Comp Computat & Stat Sci Los Alamos NM 87545 USA

The Koopman operator is a linear, infinite-dimensional operator that governs the dynamics of system observables;extended dynamic mode decomposition (EDMD) is a data-driven method for approximating the Koopman operator using functions (features) of the system state snapshots. This paper investigates an approach to EDMD in which the features used provide random approximations to a particular kernel function. The objective of this is computational economy for large data sets: EDMD is generally ill-suited for problems with large state dimension, and its dual kernel formulation (KDMD) is well-suited for such problems only if the number of data snapshots is relatively small. We discuss two specific methods for generating features: random Fourier features and the Nystrom method. The first method is a data-independent method for translation-invariant kernels only and involves random sampling in feature space;the second method is a data-dependent empirical method that may be used for any kernel and involves random sampling of data. We first discuss how these ideas may be applied in an EDMD context, as well as a means for adaptively adding random Fourier features. We demonstrate these methods on two example problems and conclude with an analysis of the relative benefits and drawbacks of each method.

关键词： dynamic mode decomposition system identification scalable algorithms dynamical systems low-dimensional modeling model inference

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A stochastic multiscale framework for modeling flow through random heterogeneous porous media

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JOURNAL OF COMPUTATIONAL PHYSICS 2009年第2期228卷 591-618页

作者： Ganapathysubramanian, B. Zabaras, N. Cornell Univ Sibley Sch Mech & Aerosp Engn Mat Proc Design & Control Lab Ithaca NY 14853 USA

Flow through porous media is ubiquitous, occurring from large geological scales down to the microscopic scales. Several critical engineering phenomena like contaminant spread, nuclear waste disposal and oil recovery rely on accurate analysis and prediction of these multiscale phenomena. Such analysis is complicated by inherent uncertainties as well as the limited information available to characterize the system. Any realistic modeling of these transport phenomena has to resolve two key issues: (i) the multi-length scale variations in permeability that these systems exhibit, and (ii) the inherently limited information available to quantify these property variations that necessitates posing these phenomena as stochastic processes. A stochastic variational multiscale formulation is developed to incorporate uncertain multiscale features. A stochastic analogue to a mixed multiscale finite element framework is used to formulate the physical stochastic multiscale process. Recent developments in linear and non-linear model reduction techniques are used to convert the limited information available about the permeability variation into a viable stochastic input model. An adaptive sparse grid collocation strategy is used to efficiently solve the resulting stochastic partial differential equations (SPDEs). The framework is applied to analyze flow through random heterogeneous media when only limited statistics about the permeability variation are given. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Stochastic partial differential equations Collocation methods Sparse grids scalable algorithms Non-linear model reduction Manifold learning Variational multiscale methods Mixed finite elements Data-driven modeling

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A COMPUTATIONAL FRAMEWORK FOR INFINITE-DIMENSIONAL BAYESIAN INVERSE PROBLEMS PART I: THE LINEARIZED CASE, WITH APPLICATION TO GLOBAL SEISMIC INVERSION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2013年第6期35卷 A2494-A2523页

作者： Tan Bui-Thanh Ghattas, Omar Martin, James Stadler, Georg Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA Univ Texas Austin Inst Computat Engn & Sci Dept Mech Engn Austin TX 78712 USA Univ Texas Austin Dept Geol Sci Austin TX 78712 USA

We present a computational framework for estimating the uncertainty in the numerical solution of linearized infinite-dimensional statistical inverse problems. We adopt the Bayesian inference formulation: given observational data and their uncertainty, the governing forward problem and its uncertainty, and a prior probability distribution describing uncertainty in the parameter field, find the posterior probability distribution over the parameter field. The prior must be chosen appropriately in order to guarantee well-posedness of the infinite-dimensional inverse problem and facilitate computation of the posterior. Furthermore, straightforward discretizations may not lead to convergent approximations of the infinite-dimensional problem. And finally, solution of the discretized inverse problem via explicit construction of the covariance matrix is prohibitive due to the need to solve the forward problem as many times as there are parameters. Our computational framework builds on the infinite-dimensional formulation proposed by Stuart [Acta Numer., 19 (2010), pp. 451-559] and incorporates a number of components aimed at ensuring a convergent discretization of the underlying infinite-dimensional inverse problem. The framework additionally incorporates algorithms for manipulating the prior, constructing a low rank approximation of the data-informed component of the posterior covariance operator, and exploring the posterior that together ensure scalability of the entire framework to very high parameter dimensions. We demonstrate this computational framework on the Bayesian solution of an inverse problem in three-dimensional global seismic wave propagation with hundreds of thousands of parameters.

关键词： Bayesian inference infinite-dimensional inverse problems uncertainty quantification scalable algorithms low rank approximation seismic wave propagation

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RIS-Aided Multiple-Input Multiple-Output Broadcast Channel Capacity

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第1期72卷 117-132页

作者： Tuan, H. D. Nasir, A. A. Dutkiewicz, E. Poor, H. V. Hanzo, L. Univ Technol Sydney Sch Elect & Data Engn Ultimo NSW 2007 Australia King Fahd Univ Petr & Minerals KFUPM Dept Elect Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals KFUPM Ctr Commun Syst & Sensing Dhahran 31261 Saudi Arabia Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

scalable algorithms are conceived for obtaining the sum-rate capacity of the reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input multiple-output (MIMO) broadcast channel (BC), where a multi-antenna base station (BS) transmits signals to multi-antenna users with the help of an RIS equipped with a massive number of finite-resolution programmable reflecting elements (PREs). As a byproduct, scalable path-following algorithms emerge for determining the sum-rate capacity of the conventional MIMO BCs, closing a long-standing open problem of information theory. The paper also develops scalable algorithms for maximizing the minimum rate (max-min rate optimization) of the users achieved by the joint design of RIS's PRE and transmit beamforming for such an RIS-aided BC. The simulations provided confirm the high performance achieved by the algorithms developed, despite their low computational complexity.

关键词： Optimization MIMO communication Optimized production technology Minimax techniques Computational modeling Sensors Linear programming Reconfigurable intelligent surface (RIS)-aided communication sum capacity mixed discrete continuous optimization scalable algorithms

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Implementation of a scalable, performance portable shallow water equation solver using radial basis function-generated finite difference methods

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2019年第4期33卷 619-631页

作者： Elliott, Samuel Kumar, Raghu Raj Prasanna Flyer, Natasha Ta, Tuan Loft, Richard Univ Colorado Appl Math Boulder CO 80309 USA Natl Ctr Atmospher Res Special Tech Projects Grp POB 3000 Boulder CO 80307 USA Natl Ctr Atmospher Res Sci Comp POB 3000 Boulder CO 80307 USA Natl Ctr Atmospher Res Computat & Informat Syst Lab Technol Dev POB 3000 Boulder CO 80307 USA Univ Mississippi Oxford MS USA Natl Ctr Atmospher Res Boulder CO USA

In this article, we describe and analyze the computational performance of a parallel shallow water equation (SWE) solver for atmospheric simulation using radial basis function-finite difference (RBF-FD) methods. The inherent "meshless" nature of RBF-FD methods provides significant numerical benefits over standard pseudospectral and traditional FD methods, but there are many challenges in terms of their performance and parallel implementation, due to RBF-FDs use of relatively large halos and unstructured indexing. With the use of reverse Cuthill-McKee node ordering and tiled transposition of the state variable matrices and RBF-FD differentiation matrices, these challenges were overcome. The RBF-FD solver was implemented for the SWE on the rotating sphere using message passing interface plus OpenMP/OpenACC to demonstrate scalability and performance portability on the three currently dominant high performance computing (HPC) architectures, namely, Intel Xeon multicore, Intel Xeon Phi manycore, and NVIDIA graphics processing unit systems.

关键词： Radial basis function-finite difference radial basis function finite differences shallow water equations reverse Cuthill-McKee message passing interface OpenMP OpenACC scalable algorithms geophysical modeling

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Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model

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PARALLEL COMPUTING 2016年第0期57卷 87-106页

作者： Rubensson, Emanuel H. Rudberg, Elias Uppsala Univ Div Comp Sci Dept Informat Technol Box 337 SE-75105 Uppsala Sweden

We present a method for parallel block-sparse matrix-matrix multiplication on distributed memory clusters. By using a quadtree matrix representation, data locality is exploited without prior information about the matrix sparsity pattern. A distributed quadtree matrix representation is straightforward to implement due to our recent development of the Chunks and Tasks programming model [Parallel Comput. 40, 328 (2014)]. The quadtree representation combined with the Chunks and Tasks model leads to favorable weak and strong scaling of the communication cost with the number of processes, as shown both theoretically and in numerical experiments. Matrices are represented by sparse quadtrees of chunk objects. The leaves in the hierarchy are block-sparse submatrices. Sparsity is dynamically detected by the matrix library and may occur at any level in the hierarchy and/or within the submatrix leaves. In case graphics processing units (GPUs) are available, both CPUs and GPUs are used for leaf-level multiplication work, thus making use of the full computing capacity of each node. The performance is evaluated for matrices with different sparsity structures, including examples from electronic structure calculations. Compared to methods that do not exploit data locality, our locality-aware approach reduces communication significantly, achieving essentially constant communication per node in weak scaling tests. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Parallel computing Sparse matrix-matrix multiplication scalable algorithms Large-scale computing Graphics processing units

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