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parallel Algorithm for Solving Constrained Global Optimization Problems 14th

14th International Conference on parallel Computing Technologies (PaCT)

作者： Barkalov, Konstantin Lebedev, Ilya Lobachevsky State Univ Nizhny Novgorod Nizhnii Novgorod Russia

ISBN: (纸本)9783319629322;9783319629315

This work considers a parallel algorithm for solving multiextremal problems with non-convex constraints. The distinctive feature of this algorithm, which does not use penalty functions, is the separate consideration of each problem constraint. The search process can be conducted by reducing the original multidimensional problem to a number of related one-dimensional problems and solving this set of problems in parallel. An experimental assessment of parallel algorithm efficiency was conducted by finding the numeric solution to several hundred randomly generated multidimensional multiextremal problems with non-convex constraints.

关键词： Global optimization Constrained problems Non-convex constraints Dimension reduction parallel algorithms

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Concurrent Estimation of Origin-Destination Flows and Calibration of Microscopic Traffic Simulation Parameters in a High-Performance Computing Cluster

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2018年第1期144卷 0页

作者： Omrani, Reza Kattan, Lina CIMA 3027 Harvester RdSuite 400 Burlington ON L7N 3G7 Canada Univ Calgary Transportat Syst Optimizat 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

This paper is aimed at developing an optimization framework for the concurrent calibration of demand and supply parameters in a dynamic traffic assignment (DTA) model. The proposed approach calibrates route choice, along with drivers' behavioral parameters, and estimates origin-destination (OD) flows in a large-scale network in a Paramics microscopic traffic simulation model. A mathematical formulation is defined to quantify the reliability of the observations. A genetic algorithm (GA) is selected as a suitable solution algorithm for the resulting nonlinear stochastic optimization problem. The application of the proposed methodology is implemented in the large-scale network in the business district core of downtown Toronto, Ontario, Canada. For this network, the emerging traffic surveillance data from in-vehicle navigation system technology provide an enriched source of disaggregated speed data. The empirical results from various experiments support the hypothesis that incorporating in-vehicle navigation system speed data can improve the calibration accuracy and minimize the reliance of the calibration process on a priori OD flows. The quality of the solution and convergence speed of a GA is further enhanced by dividing the GA population into multiple demes and running the GA on a high-performance computing cluster (HPCC) with multiple processors (i.e.,parallel distributed GA, PDGA). In addition, this research takes a further step toward analyzing the temporal variations of the driving behavior of travelers. The case study establishes an example for modelers and practitioners who are interested in calibrating a large-scale traffic simulation model. The developed simulation model for traffic has the potential to serve as a test bed on a HPCC for more efficient computation and integration with other optimization tools such as GAs. (C) 2017 American Society of Civil Engineers.

关键词： Genetic algorithm Intelligent transportation Computer simulation parallel algorithms Mathematical programming Optimization methods Calibration Dynamic equilibrium Stochastic systems

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A Communication-Avoiding parallel Algorithm for the Symmetric Eigenvalue Problem 17

A Communication-Avoiding Parallel Algorithm for the Symmetri...

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29th ACM Symposium on parallelism in algorithms and Architectures (SPAA)

作者： Solomonik, Edgar Ballard, Grey Demmel, James Hoefler, Torsten Univ Illinois Dept Comp Sci Champaign IL 61820 USA Wake Forest Univ Dept Comp Sci Winston Salem NC 27109 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Swiss Fed Inst Technol Comp Sci Dept Zurich Switzerland

ISBN: (纸本)9781450345934

Many large-scale scientific computations require eigenvalue solvers in a scaling regime where efficiency is limited by data movement. We introduce a parallel algorithm for computing the eigenvalues of a dense symmetric matrix, which performs asymptotically less communication than previously known approaches. We provide analysis in the Bulk Synchronous parallel (BSP) model with additional consideration for communication between a local memory and cache. Given sufficient memory to store c copies of the symmetric matrix, our algorithm requires Theta(root c) less interprocessor communication than previously known algorithms, for any c <= p(1/3) when using p processors. The algorithm first reduces the dense symmetric matrix to a banded matrix with the same eigenvalues. Subsequently, the algorithm employs successive reduction to O(log p) thinner banded matrices. We employ two new parallel algorithms that achieve lower communication costs for the full-to-band and band-to-band reductions. Both of these algorithms leverage a novel QR factorization algorithm for rectangular matrices.

关键词： communication cost symmetric eigenvalue problem parallel algorithms

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Urban Traffic Noise Maps under 3D Complex Building Environments on a Supercomputer

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JOURNAL OF ADVANCED TRANSPORTATION 2018年第Pt.4期2018卷 7031418.1-7031418.10页

作者： Cai, Ming Yao, Yifan Wang, Haibo Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China Guangdong Prov Key Lab Intelligent Transportat Sy Guangzhou Guangdong Peoples R China Guangdong Prov Engn Res Ctr Traff Environm Monito Guangzhou Guangdong Peoples R China Hebei Univ Technol Sch Civil & Transportat Engn Tianjin Peoples R China

The complexity of the 3D buildings and road networks gives the simulation of urban noise difficulty and significance. To solve the problem of computing complexity, a systematic methodology for computing urban traffic noise maps under 3D complex building environments is presented on a supercomputer. A parallel algorithm focused on controlling the compute nodes of the supercomputer is designed. Moreover, a rendering method is provided to visualize the noise map. In addition, a strategy for obtaining a real-time dynamic noise map is elaborated. Two efficiency experiments are implemented. One experiment involves comparing the expansibility of the parallel algorithm with various numbers of compute nodes and various computing scales to determine the expansibility. With an increase in the number of compute nodes, the computing time increases linearly, and an increased computing scale leads to computing efficiency increases. The other experiment is a comparison of the computing speed between a supercomputer and a normal computer;the computing node of Tianhe-2 is found to be six tones faster than that of a normal computer. Finally, the traffic noise suppression effect of buildings is analyzed. It is found that the building groups have obvious shielding effect on traffic noise.

关键词： supercomputer EXPANSIVITY traffic noise Ganglia Buildings road network parallel algorithms computing speed

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An N log N parallel Fast Direct Solver for Kernel Matrices 31

An <i>N</i> log <i>N</i> Parallel Fast Direct Solver for Ker...

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31st IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Yu, Chenhan D. March, William B. Biros, George Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA

ISBN: (纸本)9781538639146

Kernel matrices appear in machine learning and non-parametric statistics. Given N points in d dimensions and a kernel function that requires O(d) work to evaluate, we present an O(dN logN)-work algorithm for the approximate factorization of a regularized kernel matrix, a common computational bottleneck in the training phase of a learning task. With this factorization, solving a linear system with a kernel matrix can be done with O(N logN) work. Our algorithm only requires kernel evaluations and does not require that the kernel matrix admits an efficient global low rank approximation. Instead, our factorization only assumes low-rank properties for the off-diagonal blocks under an appropriate row and column ordering. We also present a hybrid method that, when the factorization is prohibitively expensive, combines a partial factorization with iterative methods. As a highlight, we are able to approximately factorize a dense 11M x11M kernel matrix in 2 minutes on 3,072 x86 "Haswell" cores and a 4.5Mx4.5M matrix in 1 minute using 4,352 "Knights Landing" cores.

关键词： kernel methods linear solvers machine learning parallel algorithms treecodes

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A comparative study of parallel algorithms for the girth problem

Conferences in Research and Practice in Information Technolo...

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Conferences in Research and Practice in Information Technology Series 2012年 127卷 23-30页

作者： Dinneen, Michael J. Khosravani, Masoud Wei, Kuai Department of Computer Science University of Auckland Private Bag 92019 Auckland New Zealand

ISBN: (纸本)9781921770081

In this paper we introduce efficient parallel algorithms for finding the girth in a graph or digraph, where girth is the length of a shortest cycle. We empirically compare our algorithms by using two common APIs for parallel programming in C++, which are OpenMP for multiple CPUs and CUDA for multi-core GPUs. We conclude that both hardware platforms and programming models have their benefits. © 2012, Australian Computer Society, Inc.

关键词： parallel algorithms

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parallel Sort-Based Matching for Data Distribution Management on Shared-Memory Multiprocessors 17

Parallel Sort-Based Matching for Data Distribution Managemen...

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21st IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

作者： Marzolla, Moreno D'Angelo, Gabriele Univ Bologna Dept Comp Sci & Engn DISI Bologna Italy

ISBN: (纸本)9781538640289

In this paper we consider the problem of identifying intersections between two sets of d-dimensional axis-parallel rectangles. This is a common operation that arises in many agent-based simulation studies, and is of central importance in the context of High Level Architecture (HLA), where it is at the core of the Data Distribution Management (DDM) service. Several realizations of the DDM service have been proposed;however, many of them are either inefficient or inherently sequential. We propose a parallel version of the Sort-Based Matching algorithm for shared-memory multiprocessors. SortBased Matching is one of the most efficient serial algorithms for the DDM problem, but is quite difficult to parallelize because of data dependencies. We describe the algorithm and compute its asymptotic running time;we complete the analysis by assessing its performance and scalability through extensive experiments on two commodity multicore systems based on a dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.

关键词： Data Distribution Management High Level Architecture parallel algorithms Sort-Based Matching

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Species Distribution Modeling with Scalability: The Case Study of P-GARP, a parallel Genetic Algorithm for Rule-set Production 18

Species Distribution Modeling with Scalability: The Case Stu...

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IEEE 18th International Conference on Information Reuse and Integration (IRI)

作者： Santana, Fabiana Bravo Pariente, Cesar Alberto Saraiva, Antonio Mauro Univ Canberra Fac ESTEM Canberra ACT Australia Univ Estadual Santa Cruz Dept Ciencias Exatas & Tecnol Ilheus BA Brazil Univ Sao Paulo Escola Politecn Sao Paulo Brazil

ISBN: (纸本)9781538615621

Species distribution modeling (SDM) calculates a species' probabilistic distribution by combining Environmental raster layers with species datasets. Such models can help to answer complex questions in Ecology/Biology/Health, e.g., by calculating impacts of climate changes in Biodiversity, or the potential for a disease spread (vectors' modeling). Machine learning is largely applied in SDM, being the Genetic Algorithm for Rule-set Production (GARP) one of the most reliable solutions. However, GARP's convergence needs to speedup under certain conditions (high resolution or number of layers), for which this paper proposes P-GARP, a parallel, scalable implementation of GARP. P-GARP was implemented onto a SGI Altix XE 1300 cluster with 2 quad-core processors/node. Preliminary results show an expressive 3.2/node speedup. Premature convergence is not observed in PGARP and its accuracy is very similar to GARP ' s. Effective solutions to improve this speedup in even larger scale are proposed, along with a discussion about P-GARP correctness and efficiency.

关键词： species distribution modeling ecological niche modeling genetic algorithms parallel algorithms GARP P-GARP

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A parallel Algorithm for Generating a Random Graph with a Prescribed Degree Sequence 5

A Parallel Algorithm for Generating a Random Graph with a Pr...

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IEEE International Conference on Big Data (IEEE Big Data)

作者： Bhuiyan, Hasanuzzaman Khan, Maleq Marathe, Madhav Virginia Tech Biocomplex Inst NDSSL Dept Comp Sci Blacksburg VA 24061 USA Texas A&M Univ Kingsville Dept Elect Engn & Comp Sci Kingsville TX USA Microsoft Inc Redmond WA 98052 USA

ISBN: (纸本)9781538627150

Random graphs (or networks) have gained a significant increase of interest due to its popularity in modeling and simulating many complex real-world systems. Degree sequence is one of the most important aspects of these systems. Random graphs with a given degree sequence can capture many characteristics like dependent edges and non-binomial degree distribution that are absent in many classical random graph models such as the Erdos-Renyi graph model. In addition, they have important applications in uniform sampling of random graphs, counting the number of graphs having the same degree sequence, as well as in string theory, random matrix theory, and matching theory. In this paper, we present an OpenMP-based shared-memory parallel algorithm for generating a random graph with a prescribed degree sequence, which achieves a speedup of 20.4 with 32 cores. We also present a comparative study of several structural properties of the random graphs generated by our algorithm with that of the real-world graphs and random graphs generated by other popular methods. One of the steps in our parallel algorithm requires checking the Erdos-Gallai characterization, i.e., whether there exists a graph obeying the given degree sequence, in parallel. This paper presents a non-trivial parallel algorithm for checking the Erdos-Gallai characterization, which achieves a speedup of 23 with 32 cores.

关键词： graph theory random graph generation degree sequence Erdos-Gallai characterization parallel algorithms

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A parallel Iterative Layered-Medium Integral-Equation Solver for Electromagnetic Analysis of Electronic Packages 26

A Parallel Iterative Layered-Medium Integral-Equation Solver...

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IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

作者： Liu, C. Aygun, K. Braunisch, H. Okhmatovski, V. I. Yilmaz, A. E. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Intel Corp Chandler AZ 85226 USA Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9781467364836

A parallel iterative solver for multiport network parameter extraction of large-scale electronic packages is presented. The proposed solver is based on a frequency-domain integral-equation formulation that accounts for the substrate using planar layered-medium Green's functions, conductor loss/roughness using an impedance boundary condition, and port truncations using a non-radiating lumped port model. The parallel iterative solution is accelerated by a sparse preconditioner and an FFT-based matrix-vector multiplication algorithm. A scalability study demonstrates the solver's suitability for analyzing high-fidelity and large-scale package models.

关键词： integral-equation methods layered media parallel algorithms FFT preconditioning

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