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17th International Asian School-Seminar "Optimization Problems of Complex Systems", OPCS 2021

作者： Sergeev, Kirill Migov, Denis Novosibirsk State University Novosibirsk Russia Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russia

ISBN: (纸本)9781665405621

The paper presents parallel algorithms for calculating the exact value of all-terminal reliability of a network with unreliable edges and absolutely reliable nodes. A random graph is used as a model of such network. The algorithms are based on the factorization procedure which is a well-known sequential method of a reliability calculation. parallelization of the algorithms consists in sending subgraphs, arising during the factorization of a network on the master process, to the rest of the processes that perform sequential calculation by the factorization. The basic idea of the parallel algorithms proposed is to distinguish those subgraphs, arising during the factorization in the work processes, that are relatively hard for reliability calculation. These subgraphs are sent back to the master process, which runs the computation of their reliability in a recursive way, i.e. according to the same scheme as with an initial graph. The results of the numerical experiments are given. © 2021 IEEE

关键词： parallel algorithms

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Reviewing GPU architectures to build efficient back projection for parallel geometries

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2020年第5期17卷 1331-1373页

作者： Chilingaryan, Suren Ametova, Evelina Kopmann, Anreas Mirone, Alessandro Karlsruhe Inst Technol Karlsruhe Germany Katholieke Univ Leuven Leuven Belgium Univ Manchester Manchester Lancs England ESRF Data Anal Unit Grenoble France Karlsruhe Inst Technol Inst Data Proc & Elect Data Proc Grp Karlsruhe Germany

Back-Projection is the major algorithm in Computed Tomography to reconstruct images from a set of recorded projections. It is used for both fast analytical methods and high-quality iterative techniques. X-ray imaging facilities rely on Back-Projection to reconstruct internal structures in material samples and living organisms with high spatial and temporal resolution. Fast image reconstruction is also essential to track and control processes under study in real-time. In this article, we present efficient implementations of the Back-Projection algorithm for parallel hardware. We survey a range of parallel architectures presented by the major hardware vendors during the last 10 years. Similarities and differences between these architectures are analyzed and we highlight how specific features can be used to enhance the reconstruction performance. In particular, we build a performance model to find hardware hotspots and propose several optimizations to balance the load between texture engine, computational and special function units, as well as different types of memory maximizing the utilization of all GPU subsystems in parallel. We further show that targeting architecture-specific features allows one to boost the performance 2-7 times compared to the current state-of-the-art algorithms used in standard reconstructions codes. The suggested load-balancing approach is not limited to the back-projection but can be used as a general optimization strategy for implementing parallel algorithms.

关键词： parallel algorithms Hardware architecture GPU computing Synchrotron tomography Back-projection CUDA OpenCL

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Local discontinuous Galerkin method for distributed-order time-fractional diffusion-wave equation: Application of Laplace transform

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2021年第6期44卷 4923-4937页

作者： Mohammadi-Firouzjaei, Hadi Adibi, Hojatollah Dehghan, Mehdi Amirkabir Univ Technol Dept Appl Math Fac Math & Comp Sci 424 Hafez Ave Tehran 15914 Iran

In this paper, the Laplace transform combined with the local discontinuous Galerkin method is used for distributed-order time-fractional diffusion-wave equation. In this method, at first, we convert the equation to some time-independent problems by Laplace transform. Then, we solve these stationary equations by the local discontinuous Galerkin method to discretize diffusion operators at the same time. Next, by using a numerical inversion of the Laplace transform, we find the solution of the original equation. One of the advantages of this procedure is its capability to be implemented in a parallel environment. It's another advantage is that the number of stationary problems that should be solved is much less than that is needed in time-marching methods. Finally, some numerical experiments have been provided to show the accuracy and efficiency of the method.

关键词： distributed‐ order time‐ fractional diffusion‐ wave equation Laplace transform local discontinuous Galerkin method parallel algorithms

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An Event-trigger Optimization Framework for Congestion Management 37

An Event-trigger Optimization Framework for Congestion Manag...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Li, Jianxin Fan, Weiqin Huang, Qingqiang Zhang, Lele Lin, Xiuhan Yang, Xiaoting State Grid Rizhao Electric Power Company Rizhao China University of Jinan School of Electrical Engineering Jinan China

ISBN: (纸本)9781665465366

This paper proposed an event-triggered framework to solve network congestions caused by microgrids (MGs) in regional distributed networks. Two processes are included in this framework: congestion validation process and power rescheduling process. In order to relieve the computation burden, rescheduling process is triggered only when congestions are detected in congestion validation process. DC optimal power flow based optimization model is used to describe congestion validation process. And then power rescheduling process can be formulated distributed optimization problem for multiple microgrids, which can be solved by the alternating direction method of multipliers (ADMM) algorithm. Finally, simulations are implemented to illustrate the reasonability and effectiveness of the proposed framework. Results show that the proposed framework could effectively solve the congestions with transaction diversity guaranteed. © 2022 IEEE.

关键词： parallel algorithms

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MPI-Based Simulation of the Shallow Water Model using the Finite Volume Characteristics Scheme 6

MPI-Based Simulation of the Shallow Water Model using the Fi...

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6th International Conference on Computer, Software and Modeling, ICCSM 2022

作者： Moussa, Ziggaf Imad, Kissami ENSAO LMCS Complexe Universitaire B.P. 669 Oujda60000 Morocco MSDA Mohammed VI Polytechnic University Lot 660 Ben Guerir43150 Morocco Universite Sorbonne Paris Nord LAGA CNRS UMR 7539 VilletaneuseF-93430 France

ISBN: (数字)9781665454865

ISBN: (纸本)9781665454865

A parallel algorithm for solving the 2D shallow water equations coupled with the convection-diffusion equation has been developed, in order to demonstrate the capability and performance of our parallel approach while maintaining very good accuracy of the results obtained. The numerical scheme used is written in a non-uniform triangular grid formalism, which allows for the complexity of the geometry of the computational domain used. This approach is based on both predictor and corrector stages. The predictor one uses the method of characteristics to reconstruct the numerical fluxes, whereas the corrector stage recovers the conservation equations. Numerical results are presented for a pollutant transport in a squared cavity. © 2022 IEEE.

关键词： parallel algorithms

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parallel Minimum Cuts in O(m log2n) Work and Low Depth 21

Parallel Minimum Cuts in O(m log2n) Work and Low Depth

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33rd ACM Symposium on parallelism in algorithms and Architectures, SPAA 2021

作者： Anderson, Daniel Blelloch, Guy E. Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450380706

We present a randomized O(m log^2 n) work, O(polylog n) depth parallel algorithm for minimum cut. This algorithm matches the work bounds of a recent sequential algorithm by Gawrychowski, Mozes, and Weimann [ICALP'20], and improves on the previously best parallel algorithm by Geissmann and Gianinazzi [SPAA'18], which performs O(m log^4 n) work in O(polylog n) depth. Our algorithm makes use of three components that might be of independent interest. Firstly, we design a parallel data structure that efficiently supports batched mixed queries and updates on trees. It generalizes and improves the work bounds of a previous data structure of Geissmann and Gianinazzi and is work efficient with respect to the best sequential algorithm. Secondly, we design a parallel algorithm for approximate minimum cut that improves on previous results by Karger and Motwani. We use this algorithm to give a work-efficient procedure to produce a tree packing, as in Karger's sequential algorithm for minimum cuts. Lastly, we design an efficient parallel algorithm for solving the minimum 2-respecting cut problem. © 2021 Owner/Author.

关键词： parallel algorithms

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DPTL+: Efficient parallel triangle listing on batch-dynamic graphs 37

DPTL+: Efficient parallel triangle listing on batch-dynamic ...

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37th IEEE International Conference on Data Engineering, ICDE 2021

作者： Yu, Michael Qin, Lu Zhang, Ying Zhang, Wenjie Lin, Xuemin University of New South Wales Australia University of Technology Sydney AAII Australia

ISBN: (纸本)9781728191843

Triangle listing is an important topic in many practical applications. We have observed that this problem has not yet been studied systematically in the context of batch-dynamic graphs. In this paper, we aim to fill this gap by developing novel and efficient parallel solutions. Specifically, given a graph G and a batch-update of edges B, we report the updated triangles (deleted triangles and new triangles) resulting from the batch of updates. We notice that it is cost expensive to directly apply state-of-the-art triangle listing algorithms because they are designed to enumerate the complete set of triangles from a given graph, whereas only the updated ones are the relevant output for our problem setting. In this paper, we developed an efficient algorithm, namely DPTL, based on a newly designed orientation technique, which only outputs the updated triangles while ensuring that each triangle solution is identified without any duplicate solutions. We follow up by taking advantage of a graph's degree distributions and designed a more sophisticated algorithm, namely DPTL+. We show that DPTL+ can achieve the best performance in terms of both practical performance and theoretical time complexity. Our comprehensive experiments over 28 real-life large graphs show the superior performance of the DPTL+ algorithm when compared against DPTL and two baseline solutions. Theoretically, we also show that DPTL+ has a time complexity of Θ(∑〈u, v〉∈Bmin{deg(u), deg(v)}+m) where deg(x) is the degree of a vertex x, and m is the number of edges adjacent to the vertices in the batch-update. This time complexity is more promising than that of other solutions. © 2021 IEEE.

关键词： Conferences Data engineering Time complexity parallel algorithms

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Solving tridiagonal Toeplitz systems of linear equations on GPU-accelerated computers

Solving tridiagonal Toeplitz systems of linear equations on ...

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作者： Dmitruk, Beata Stpiczyński, Przemyslaw Institute of Computer Science Maria Curie-Sklodowska University Akademicka 9 Lublin20-033 Poland

The aim of this article is to show that solvers for tridiagonal Toeplitz systems of linear equations can be efficiently implemented for a variety of modern GPU-accelerated and multicore architectures using OpenACC. We consider two parallel algorithms for solving such systems with special assumptions about coefficient matrices. As the first algorithm, we propose a new, faster implementation of the divide and conquer method. The next algorithm is a new, vectorizable algorithm based on a recently introduced sequential method. We consider the use of both column-wise and row-wise storage formats for two-dimensional arrays and show how to efficiently convert between these two formats using cache memory and improve the overall performance of our implementations. We also show how to tune the performance by predicting the best values of the methods' parameters. Numerical experiments performed on Intel CPUs and Nvidia GPUs show that our new implementations achieve relatively good performance and accuracy. © 2021 John Wiley & Sons Ltd.

关键词： parallel algorithms

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A robust Delaunay-AFT based parallel method for the generation of large-scale fully constrained meshes

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COMPUTERS & STRUCTURES 2020年 228卷

作者： Yu, Fei Zeng, Yan Guan, Z. Q. Lo, S. H. Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China

Making full use of a sequential Delaunay-AFT mesher, a parallel method for the generation of large-scale tetrahedral meshes on distributed-memory machines is developed. To generate meshes with the required and the preserved properties, a Delaunay-AFT based domain decomposition (DD) technique is employed. Starting from the Delaunay triangulation (DT) covering the problem domain, this technique creates a layer of elements dividing the domain into several zones. The initially coarsely meshed domain is partitioned into DTs of subdomains which can be meshed in parallel. When the size of a subdomain is smaller than a user-specified threshold, it will be meshed with the standard Delaunay-AFT mesher. A two-level DD strategy is designed to improve the parallel efficiency of this algorithm. A dynamic load balancing scheme is also implemented using the Message Passing Interface (MPI). Out-of-core meshing is introduced to accommodate excessive large meshes that cannot be handled by the available memory of the computer (RAM). Numerical tests are performed for various complex geometries with thousands of surface patches. Ultra-large-scale meshes with more than ten billion tetrahedral elements have been created. Moreover, the meshes generated with different numbers of DD operations are nearly identical in quality: showing the consistency and the stability of the automatic decomposition algorithm. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Finite element mesh generation parallel algorithms Domain decomposition Delaunay triangulations Delaunay-AFT Out-of-core

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Hierarchical Model of parallel Metaheuristic Optimization algorithms 13

Hierarchical Model of Parallel Metaheuristic Optimization Al...

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13th International Symposium on Intelligent Systems (INTELS)

作者： Seliverstov, E. Y. Karpenko, A. P. Bauman Moscow State Univ Ul Baumanskaya 2 Ya5 Moscow 105005 Russia

The paper introduces a novel model of parallel metaheuristic optimization algorithms. The hierarchical graph model of a parallel optimization algorithm is proposed. It consists of the model for a parallel optimization algorithm at the top level of the hierarchy and the model for a sequential optimization algorithm at the bottom level. The unified representation of a metaheuristic optimization algorithm, which allows representing a class of metaheuristic algorithms, is used. The extension of the proposed model to the parametric hierarchical model is proposed. Graph model transformations for a parallel algorithm analysis and synthesis are introduced. The representation of several metaheuristic algorithms with the proposed model is discussed. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： evolutionary algorithms metaheuristic optimization parametric optimization parallel algorithms particle swarm optimization

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