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An Improved Method for Reliability Evaluation of Two-Terminal Multistate Networks Based on State Space Decomposition

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IEEE TRANSACTIONS ON RELIABILITY 2021年第3期70卷 1084-1095页

作者： Bai, Guanghan Liu, Tao Zhang, Yun-an Tao, Junyong Natl Univ Def Technol Coll Intelligence Sci & Technol Lab Sci & Technol Integrated Logist Support Changsha 410073 Peoples R China

This article presents an improved state space decomposition (SSD) method for reliability evaluation of multistate networks. We observe that the decomposition process of the existing SSD is a sequential decomposition process. However, the SSD has the basis of parallel operations, as the decomposition process from each unspecified state is independent of each other. In addition, the existing SSD methods applied heuristic to select a proper minimal path vector (d-MP). When there is a tie during the process, the tie is broken arbitrarily. However, we find that different d-MPs with same function value of the current heuristic affect the efficiency of the decomposition procedure. Based on these observations, an improved SSD method is developed for reliability evaluation of multistate networks. First, a parallel mechanism is proposed and incorporated into the SSD algorithm. Second, several improved heuristics, namely R1, R2, R3, and R4, are developed to select a proper d-MP. The experimental results show that the proposed SSD method can improve the efficiency for reliability evaluation of multistate networks, which provides the reliability engineers and facility managers a more powerful tool for the design and maintenance of more complex multistate networks.

关键词： Limiting Reliability theory Telecommunication network reliability Heuristic algorithms Reliability engineering Indexes Minimal path vectors parallel algorithm state-space decomposition multistate reliability two-terminal networks

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Speeding up Genetic Programming Based Symbolic Regression Using GPUs 19th

Speeding up Genetic Programming Based Symbolic Regression Us...

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19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Zhang, Rui Lensen, Andrew Sun, Yanan Sichuan Univ Chengdu 610000 Peoples R China Victoria Univ Wellington Wellington 6140 New Zealand

ISBN: (纸本)9783031208614;9783031208621

Symbolic regression has multiple applications in data mining and scientific computing. Genetic Programming (GP) is the mainstream method of solving symbolic regression problems, but its execution speed under large datasets has always been a bottleneck. This paper describes a CUDA-based parallel symbolic regression algorithm that leverages the parallelism of the GPU to speed up the fitness evaluation process in symbolic regression. We make the fitness evaluation step fully performed on the GPU and make use of various GPU hardware resources. We compare training time and regression accuracy between the proposed approach and existing symbolic regression frameworks including gplearn, TensorGP, and KarooGP. The proposed approach is the fastest among all the tested frameworks in both synthetic benchmarks and large-scale benchmarks.

关键词： Symbolic regression Genetic programming parallel algorithm Graphics processing unit (GPU) Compute unified device architecture (CUDA)

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A single-tree algorithm to compute the Euclidean minimum spanning tree on GPUs 51

A single-tree algorithm to compute the Euclidean minimum spa...

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51st International Conference on parallel Processing (ICPP)

作者： Prokopenko, Andrey Sao, Piyush Lebrun-Grandie, Damien Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9781450397339

Computing the Euclidean minimum spanning tree (Emst) is a computationally demanding step of many algorithms. While work-efficient serial and multithreaded algorithms for computing Emst are known, designing an efficient GPU algorithm is challenging due to a complex branching structure, data dependencies, and load imbalances. In this paper, we propose a single-tree Boruvka-based algorithm for computing Emst on GPUs. We use an efficient nearest neighbor algorithm and reduce the number of the required distance calculations by avoiding traversing subtrees with leaf nodes in the same component. The developed algorithms are implemented in a performance portable way using ArborX, an open-source geometric search library based on the Kokkos framework. We evaluate the proposed algorithm on various 2D and 3D datasets, show and compare it with the current state-of-the-art open-source CPU implementations. We demonstrate 4-24x speedup over the fastest multi-threaded implementation. We prove the portability of our implementation by providing results on a variety of hardware: AMD EPYC 7763, Nvidia A100 and AMD MI250X. We show scalability of the implementation, computing Emst for 37 million 3D cosmological dataset in under a 0.5 second on a single A100 Nvidia GPU.

关键词： Euclidean minimum spanning tree parallel algorithm GPU

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parallel algorithm FOR SECOND-ORDER RESTRICTED WEAK INTEGER COMPOSITION GENERATION FOR SHARED MEMORY MACHINES

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parallel PROCESSING LETTERS 2013年第3期23卷

作者： Page, Daniel R. Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada

In 2012, Page presented a sequential combinatorial generation algorithm for generalized types of restricted weak integer compositions called second order restricted weak integer compositions. Second order restricted weak integer compositions cover various types of restricted weak integer compositions of n parts such as integer compositions, bounded compositions, and part wise integer compositions. In this paper, we present a parallel algorithm that derives from our parallelization of Page's sequential algorithm with a focus on load balancing for shared memory machines.

关键词： parallel algorithm combinatorial algorithm generation algorithm computational number theory weak integer compositions restricted weak integer compositions

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On the Resonances of an Inhomogeneous Dielectric Ball Coated with Graphene

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Technical Physics 2025年 1-6页

作者： Skvortsov, O. S. Penza State University Penza Russia

The purpose of the work is the development, software implementation, and testing of algorithms designed to find resonant frequencies in an inhomogeneous dielectric ball coated with graphene. The relevance of the task is due to the widespread use of graphene in engineering due to its unique properties. The Galerkin method is applied to discretize a vector integrodifferential equation; piecewise-constant basis vector functions are determined on a three-dimensional body and on its surface, and a parallel algorithm for solving the problem is developed using the MSMPI library. The effectiveness of the parallel algorithm, which makes it possible to detect the sought resonances under specified conditions, has been confirmed. An effective parallel algorithm has been created for discretizing the integrodifferential equation, finding the determinant of the matrix, and searching for resonant frequencies.

关键词： resonant frequency inhomogeneous dielectric body graphene coating singular integrodifferential equation basis functions Galerkin method dielectric ball parallel algorithm

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Primal-dual algorithms for the Capacitated Single Allocation p-Hub Location Problem

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International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 1-17页

作者： Matos, Telmo Ciicesi Escola Superior de Tecnologia e Gestão Politécnico Do Porto Porto Portugal

The Hub Location Problems (HLP) have gathered great interest due to the complexity and to the many applications in industry such as aviation, public transportation, telecommunications, among others. The HLP have many variants regarding allocation (single or multiple) and capacity (uncapacitated or capacitated). This paper presents a variant of the HLP, encompassing single allocation with capacity constraints. The Capacitated Single Allocation p-Hub Location Problem (CSApHLP) objective consists on determine the set of p hubs in a network that minimizes the total cost of allocating all the non-hub nodes to the p hubs. In this work, it is proposed a sophisticated RAMP approach (PD-RAMP) to improve the results obtained previously by the simple version (Dual-RAMP). Thus, a parallel implementation is conducted to assess the effectiveness of a parallel RAMP model applied to the CSApHLP. The first algorithm, the sequential PD-RAMP, incorporates Dual-RAMP with a Scatter Search procedure to create a Primal-Dual RAMP approach. The second algorithm, the parallel PD-RAMP, also take advantage of the dual and primal, parallelizing the primal side of the problem and interconnecting both sides as it is expected in the RAMP sequential algorithm. The quality of the results carried out on a standard testbed shows that the PD-RAMP approach managed to improve the state-of-the-art algorithms for the CSApHLP. © 2022 - IOS Press. All rights reserved.

关键词： CSApHLP lagrangean relaxation parallel algorithm RAMP

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An Improved Seismic Damage Assessment Model for Buildings Based on parallel algorithms 5

An Improved Seismic Damage Assessment Model for Buildings Ba...

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5th International Conference on Environmental Science and Civil Engineering (ESCE)

作者： Feng, Biao Lan, Riqing Yu, Shizhuo China Earthquake Adm Inst Engn Mech Key Lab Earthquake Engn & Engn Vibrat Beijing Peoples R China

The efficiency of post-quake emergency management depends heavily on the speed of seismic damage assessment software, which depends on the evaluation algorithm essentially. Traditional seismic damage assessment models based on sequence algorithms usually need tens of minutes to get visualized results. More efficient computing models for seismic damage assessment are demanded urgently. General parallel evaluation algorithm based on GPU computing framework was investigated by the authors, and an improved seismic damage assessment model for buildings was proposed in this paper. We designed a three-level parallel scheme to accelerate the computing and visualization. At the top level, the earthquake area is divided into smaller regions, such as cities or counties. At the middle level, fundamental geographic data for each region is split into layers by features. At the bottom level, buildings are grouped into several types, which can be processed by GPU cores simultaneously. The developed model can estimate and analyze the seismic damage of buildings and the spatial distribution of victims in earthquake region more rapidly than traditional models.

关键词： parallel algorithm seismic damage assessment model buildings GPU

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An efficient parallel semi-implicit solver for anisotropic thermal conduction in the solar corona

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ASTRONOMY AND COMPUTING 2020年 30卷

作者： Ye, J. Shen, C. Lin, J. Mei, Z. Chinese Acad Sci Yunnan Observ POB 110 Kunming 650216 Yunnan Peoples R China Chinese Acad Sci Ctr Astron Mega Sci Beijing 100012 Peoples R China Smithsonian Astrophys Observ 60 Garden StMS 15 Cambridge MA 02138 USA Univ Chinese Acad Sci Beijing 100049 Peoples R China

Anisotropic thermal conduction plays an important role in determining the structure of the hot plasma in the solar corona. When hot plasma appears, the conductivity rises with temperature and becomes highly nonlinear. Explicit solvers for parabolic problems often lead to much smaller time-steps limited by a Courant-Friedrichs-Lewy (CFL) condition in comparison with hyperbolic Magnetohydrodynamics (MHD) equations. In this work, we present a pseudo-linear, directionally-split, semi-implicit method allowing for large time-steps as well as the optimized parallelization algorithm, integrated with the MHD solver. Our scheme can perfectly preserve the monotonicity and the geometry of shocks and discontinuities in complex MHD problems. Two sets of numerical tests show that an increase in time step of similar to 600 can be easily achieved with an acceptable error by our scheme compared to explicit methods, and the use of large time-steps can still follow fast dynamic processes reliably. In addition, the extendibility studies have proven that the associated parallel efficiency is comparably high. This method is also useful for any kind of time-dependent conductivity problems for the solar applications in the future. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Magnetohydrodynamics Spitzer conductivity Anisotropic diffusion Numerical methods parallel algorithm

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Variational Data Assimilation for Two Interface Problems

Variational Data Assimilation for Two Interface Problems

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作者： Li, Xuejian Missouri University of Science and Technology

学位级别：Ph.D., Doctor of Philosophy

Variational data assimilation (VDA) is a process that uses optimization techniques to determine an initial condition of a dynamical system such that its evolution best fits the observed data. In this dissertation, we develop and analyze the variational data assimilation method with finite element discretization for two interface problems, including the Parabolic Interface equation and the Stokes-Darcy equation with the Beavers-Joseph interface condition. By using Tikhonov regularization and formulating the VDA into an optimization problem, we establish the existence, uniqueness and stability of the optimal solution for each concerned case. Based on weak formulations of the Parabolic Interface equation and Stokes-Darcy equation, the dual method and Lagrange multiplier rule are utilized to derive the first order optimality system (OptS) for both the continuous and discrete VDA problems, where the discrete data assimilations are built on certain finite element discretization in space and the backward Euler scheme in time. By introducing auxiliary equations, rescaling the optimality system, and employing other subtle analysis skills, we present the finite element convergence estimation for each case with special attention paid to recovering the properties missed in between the continuous and discrete OptS. Moreover, to efficiently solve the OptS, we present two classical gradient methods, the steepest descent method and the conjugate gradient method, to reduce the computational cost for well-stabilized and ill-stabilized VDA problems, respectively. Furthermore, we propose the time parallel algorithm and proper orthogonal decomposition method to further optimize the computing efficiency. Finally, numerical results are provided to validate the proposed methods.

关键词： Data assimilation Numerical methods Optimization parallel algorithm Partial differential equation Proper orthogonal decomposition

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parallel Iterative Methods for Solving the Split Common Fixed Point Problem in Hilbert Spaces

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NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION 2020年第7期41卷 778-805页

作者： Reich, Simeon Truong Minh Tuyen Nguyen Minh Trang Technion Israel Inst Technol Dept Math Haifa Israel Thai Nguyen Univ Sci Dept Math & Informat Thai Nguyen Vietnam Thai Nguyen Univ Technol Fac Int Training Thai Nguyen Vietnam

We introduce three new parallel algorithms for solving the split common fixed point problem in Hilbert spaces. Those iterative methods for solving split common fixed point problems which involve step sizes that depend on the norm of a given bounded linear operator are often not easy to implement because one has to compute the norm of this operator. Therefore, in addition to two such algorithms, we also propose a new iterative method involving step sizes which are selected in such a way that the implementation of the method does not require the computation or estimation of the norm of the given operator. Several corollaries of our main results regarding the solution of multiple-set split feasibility, split common null point, split variational inequality and split mixed equilibrium problems are also presented.

关键词： Metric projection nonexpansive mapping parallel algorithm split common fixed point problem

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