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data parallel VISUAL RECONSTRUCTION AND PARTITIONING algorithms

parallel Processing Letters 1993年第3期3卷 267-277页

作者： PAUL C. XIROUCHAKIS PEARL Y. WANG OPHIR FRIEDER J.J.M.A. Inc. 2301 S. Jefferson Davis Hwy Suite 505 Arlington Virginia 22202 USA Computer Science Department George Mason University Fairfax Virginia 22030–4444 USA This research was partially supported by the National Science Foundation under contract number IRI-9357785.

data parallel visual reconstruction and partitioning algorithms and the associated code are developed for a vector random access machine (V-RAM). Finite element algorithms are constructed for solving the one-dimensional visual reconstruction problem with the input data consisting of a symmetrical top hat loading for the modeling of interacting step discontinuities. The advantage of the V-RAM implementation is the general code applicability to a variety of architectures. A specific implementation is performed on a Distributed Array Processor (DAP) simulator on the VAX 6000–420. Execution times on the DAP simulator are obtained and are found to be in agreement with the algorithmic complexities of the V-RAM code.

关键词： data parallel algorithms visual reconstruction and partitioning algorithms finite element methods image processing numerical methods

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data parallel supervised classification algorithms on multispectral images

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 1996年第7期10卷 751-767页

作者： Johansson, T Bengtsson, E UPPSALA UNIV CTR IMAGE ANALS-75237 UPPSALASWEDEN

In remote sensing the intensities from a multispectral image are used in a classification scheme to distinguish different ground cover from each other. An example is given where different soil types are classified. A digitized complete scene from a satellite sensor consists of a large amount of data and in future image sensors the resolution and the number of spectral bands will increase even further. data parallel computers are therefore well-suited for these types of classification algorithms. This article will focus on three supervised classified algorithms: the Maximum Likelihood, the K-Nearest Neighbor and the Backpropagation algorithm, together with their parallel implementations. They are implemented on the Connection Machine/200 in the high-level language C*. The algorithms are finally tested and compared on an image registered over western Estonia.

关键词： data parallel algorithms classification satellite images remote sensing backpropagation K-nearest neighbor maximum likelihood

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Scalable Contour Tree Computation by data parallel Peak Pruning

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021年第4期27卷 2437-2454页

作者： Carr, Hamish A. Weber, Gunther H. Sewell, Christopher M. Rubel, Oliver Fasel, Patricia Ahrens, James P. Univ Leeds Leeds LS2 9JT W Yorkshire England Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Los Alamos Natl Lab Los Alamos NM 87545 USA

As data sets grow to exascale, automated data analysis and visualization are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of O(lg V lgt) parallel steps and O(V lg V) work for data with V samples and t contour tree supernodes, and implementations with more than 30x parallel speed up on both CPU using TBB and GPU using Thrust and up 70x speed up compared to the serial sweep and merge algorithm.

关键词： Topological analysis contour tree merge tree data parallel algorithms computer graphics computational geometry and object modeling curve surface solid and object representations simulation and modeling simulation output analysis

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PDAC - A data-parallel ALGORITHM FOR THE PERFORMANCE ANALYSIS OF CLOSED QUEUING-NETWORKS

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parallel COMPUTING 1993年第12期19卷 1345-1358页

作者： HANSON, FB MEI, JD TIER, C XU, HH Laboratory for Advanced Computing University of Illinois at Chicago 851 S. Morgan M/C 249 Chicago IL 60607-7045 USA

A parallel distribution analysis by chain algorithm (PDAC) is presented for the performance analysis of closed, multiple class queueing networks. The PDAC algorithm uses data parallel computation of the summation indices needed to compute the joint queue length probabilities. The computational cost of the PDAC algorithm is shown to be of polynomial order with a lower degree than the cost of the serial implementation of the DAC algorithm. Examples are presented comparing the PDAC algorithm with the DAC algorithm to illustrate its advantages and limitations.

关键词： data parallel algorithms CLOSED QUEUING NETWORKS PERFORMANCE ANALYSIS

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THE FINITE ELEMENT METHOD ON A data parallel COMPUTING SYSTEM

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International Journal of High Speed Computing 1989年第1期1卷 29-44页

作者： KAPIL K. MATHUR S. LENNART JOHNSSON Thinking Machines Corporation 245 First Street Cambridge MA 02142 USA Also Departments of Computer Science and Electrical Engineering Yale University New Haven CT 06520 USA.

A data parallel implementation of the finite element method on the Connection Machine system CM-2 ® is presented. This implementation assumes that the elementary unit of data is an unassembled nodal point. In the context of the CM-2, each virtual processor represents an unassembled nodal point and nodal points shared between elements are replicated on different virtual processors. An algorithm for computing each elemental stiffness matrix concurrently, as well as different elemental stiffness matrices concurrently, without inter-processor communication is presented. The performance of the elemental stiffness matrix computation is in the range 1.6–1.9 GFlops s −1 . The sparse system of linear equations that results from the finite element discretization has been solved by a conjugate gradient method with a diagonal preconditioner. The rate of convergence of the conjugate gradient iterations for boundary conditions which correspond to uniaxial deformations depends nonlinearly on the order of interpolation of the elements and linearly on the mesh discretization. Sample code segments are provided to illustrate the programming environment on a data parallel architecture.

关键词： finite element method data parallel algorithms preconditioned conjugate gradient method supercomputing

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SUPERB AND VIENNA-FORTRAN

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parallel COMPUTING 1994年第10-11期20卷 1487-1517页

作者： ZIMA, HP BREZANY, P CHAPMAN, BM Institute for Software Technology and Parallel Systems University of Vienna Br&uuml nner Strasse 72 A-1210 Vienna Austria

Distributed-memory systems are powerful tools for solving large-scale scientific and engineering problems. However these machines are difficult to program since the data have to be distributed across the processors and message-passing operations must be inserted for communicating non-local data. In this paper, we discuss SUPERB and Vienna Fortran, two related developments with the objective of providing the user with a higher level programming paradigm while not sacrificing target code performance. The parallelization system SUPERB was developed in the German supercomputer project SUPRENUM from 1985 to 1989. It is based on the Single-Program-Multiple-data (SPMD) paradigm, allows the use of global addresses, and automatically inserts the necessary communication statements, given a user-supplied data distribution. SUPERB was the first implemented system that translated sequential Fortran 77 into explicitly parallel message-passing Fortran. As a result of the experiences with SUPERB and related research, the language Vienna Fortran was designed within the ESPRIT project GENESIS, in a joint effort of the University of Vienna and ICASE, Nasa Langley Research Center. Vienna Fortran is a machine-independent language extension to Fortran, which includes a broad range of features for the high-level support of advanced application development for distributed-memory multiprocessors. It has significantly influenced the development of High Performance Fortran, a first attempt of language standardization in this area.

关键词： DISTRIBUTED-MEMORY MULTIPROCESSOR SYSTEMS NUMERICAL COMPUTATION data parallel algorithms data DISTRIBUTION PROGRAM ANALYSIS OPTIMIZATION

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Simulating heterogeneous behaviours in complex systems on GPUs

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SIMULATION MODELLING PRACTICE AND THEORY 2018年 83卷 3-17页

作者： Chimeh, Mozhgan K. Richmond, Paul Univ Sheffield Sheffield S Yorkshire England

Agent Based Modelling (ABM) is an approach for modelling dynamic systems and studying complex and emergent behaviour. ABMs have been widely applied in diverse disciplines including biology, economics, and social sciences. The scalability of ABM simulations is typically limited due to the computationally expensive nature of simulating a large number of individuals. As such, large scale ABM simulations are excellent candidates to apply parallel computing approaches such as Graphics Processing Units (GPUs). In this paper, we present an extension to the FLAME GPU 1 [1] framework which addresses the divergence problem, i.e. the challenge of executing the behaviour of non-homogeneous individuals on vectorised GPU processors. We do this by describing a modelling methodology which exposes inherent parallelism within the model which is exploited by novel additions to the software permitting higher levels of concurrent simulation execution. Moreover, we demonstrate how this extension can be applied to realistic cellular level tissue model by benchmarking the model to demonstrate a measured speedup of over 4x. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Agent Based Modeling GPGPU data parallel algorithms Simulation FLAME GPU

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POLYMORPHIC PROCESSOR ARRAYS

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 1993年第5期4卷 490-506页

作者： MARESCA, M INT COMP SCI INST I-16145 GENOAITALY

A Polymorphic Processor Array (PPA) is a two-dimensional mesh-connected array of processors, in which each processor is equipped with a switch able to interconnect its four NEWS ports. PPA is an abstract architecture based upon the experience acquired in the design and in the implementation of a VLSI chip, namely the Polymorphic Torus (PT) chip, and, as a consequence, it only includes capabilities that have been proved to be supported by cost-effective hardware structures. The main claims of PPA are that 1) it models a realistic class of parallel computers, 2) it supports the definition of high level programming models, 3) it supports virtual parallelism and 4) it supports low complexity algorithms in a number of application fields. In this paper we present both the PPA computation model and the PPA programming model;we show that the PPA computation model is realistic by relating it to the design of the PT chip and show that the PPA programming model is scalable by demonstrating that any algorithm having O(p) complexity on a virtual PPA of size square-root m x square-root m, has O(kp) complexity on a PPA of size square-root n x square-root n, with m = kn and k integer. We finally show some application algorithms in the area of numerical analysis and graph processing.

关键词： data parallel algorithms MASSIVELY parallel COMPUTERS MESH CONNECTED COMPUTERS parallel COMPUTATION MODELS parallel PROGRAMMING MODELS RECONFIGURABLE parallel COMPUTERS SIMD ARCHITECTURES

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HIGHLY parallel SPARSE CHOLESKY FACTORIZATION

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SIAM JOURNAL ON SCIENTIFIC AND STATISTICAL COMPUTING 1992年第5期13卷 1151-1172页

作者： GILBERT, JR SCHREIBER, R NASA AMES RES CTRADV COMP SCI RES INSTMOFFETT FIELDCA 94035

This paper develops and compares several fine-grained parallel algorithms to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed-memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special-purpose algorithms in which the matrix structure conforms to the connection structure of the machine, this paper focuses on matrices with arbitrary sparsity structure. The most promising alternative is a supernodal, multifrontal algorithm whose inner loop performs several dense factorizations simultaneously on a two-dimensional grid of processors. The key subroutine is a fine-grained parallel, dense-factorization algorithm. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and used to analyze these algorithms. Asymptotic analysis combined with experimental measurement of parameters is accurate enough to be useful in choosing among alternative algorithms for a complicated problem.

关键词： SPARSE MATRIX algorithms CHOLESKY FACTORIZATION SUPERNODAL FACTORIZATION MULTIFRONTAL FACTORIZATION SYSTEMS OF LINEAR EQUATIONS parallel COMPUTING data parallel algorithms CHORDAL GRAPHS CLIQUE TREES CONNECTION MACHINE PERFORMANCE ANALYSIS

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EFFICIENT SPARSE CHOLESKY FACTORIZATION ON A MASSIVELY-parallel SIMD COMPUTER

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 1995年第4期16卷 934-950页

作者： MANNE, F HAFSTEINSSON, H UNIV ICELAND DEPT COMP SCIREYKJAVIKICELAND

We investigate the effect of load balancing when performing Cholesky factorization on a massively parallel SIMD computer. In particular we describe a supernodal algorithm for performing sparse Cholesky factorization. The way the matrix is mapped onto the processors has significant effect on its efficiency. We show that this assignment problem can be modeled as a graph coloring problem in a weighted graph. By a simple greedy algorithm, we obtain substantial speedup compared with previously suggested data mapping schemes. Experimental runs have been made on a 16K processor MasPar MP-2 parallel computer using symmetric test matrices with irregular sparsity structure. On these problems our implementation achieves performance rates of well above 200 Mflops in double precision arithmetic.

关键词： SPARSE MATRIX algorithms CHOLESKY FACTORIZATION SYSTEMS OF LINEAR EQUATIONS parallel COMPUTING data parallel algorithms MASPAR GRAPH COLORING

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