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IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Masaki Tao Koji Nakano Yasuaki Ito Ryota Yasudo Masaru Tatekawa Ryota Katsuki Takashi Yazane Yoko Inaba Department of Information Engineering Hiroshima University Kagamiyama 1-4-1 Higashihiroshima Japan Research and Development Headquarters NTT DATA Corporation Toyosu Center Bldg Annex 3-9 Toyosu 3-chome Koto-ku Tokyo Japan

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

The main contribution of this paper is to present a simple exhaustive search algorithm for the quadratic unconstraint binary optimization (QUBO) problem. It computes the values of the objective function E(X) for all n-bit input vector X in O(2 n ) time. Since Ω(2 n ) time is necessary to output E(X) for all 2 n vectors X, this sequential algorithm is optimal. We also present a work-time optimal parallel algorithm running O(log n) time using 2 n / log n processors on the CREW-PRAM. This parallel algorithm is work optimal, because the total number of computational operations is equal to the running time of the optimal sequential algorithm. Also, it is time optimal because any parallel algorithm using any large number of processors takes at least Ω(log n) time for evaluating E(X). Further, we have implemented this parallel algorithm to run on the GPU. The experimental results on NVIDIA GeForce RTX 2080Ti GPU show that our GPU implementation runs more than 1000 times faster than the sequential algorithm running on Intel Corei7-8700K CPU(3.70GHz) for the QUBO with nbit vector whenever n ≥ 33. We also compare our exhaustive search parallel algorithm with several non-exhaustive search approaches for solving the QUBO including D-Wave 2000Q quantum annealer, simulated annealing algorithm, and Gurobi optimizer.

关键词： Graphics processing units parallel algorithms Computational modeling Simulated annealing Mathematical model

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A parallel Algorithm for Eukaryotic Promoter Recognition 2nd

A Parallel Algorithm for Eukaryotic Promoter Recognition

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2nd International Congress on High-Performance Computing and Big Data Analysis, TopHPC 2019

作者： Langroudi, Seyyed Mohammad Shaheri Hamidi, Hamid Reza Kermanshahani, Shokooh Computer Engineering Department Imam Khomeini International University Qazvin Iran

ISBN: (纸本)9783030334949

One of the interesting problems in Bioinformatics is finding transcription start site in a gene. In fact, finding this site which separate promoter region from coding sequence, actually will end to promoter prediction. This leads to activate or inactivate some parts of gene which plays an important role after being translated to protein sequence. While traditional methods are reliable ways for promoter prediction, because of the large number of sequences and too much of information, it is not possible to study these sequences by those methods. Although some of these sequences have been already recognized and their information has been stored in big databases like NCBI, there are some sequences which their promoter regions have not been identified yet. This research aimed to design a parallel algorithm for one of the known promoter prediction algorithms, Ohler. We attempt to reduce the response time of Ohler algorithm, consequently increases the number of test samples, and improves the accuracy of the algorithm. The experimental results show that we have succeeded to achieve our purpose. © 2019, Springer Nature Switzerland AG.

关键词： parallel algorithms

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parallel implementation of the conjugate gradient merhod for solving the inverse gravimetry problem on GPU 18

Parallel implementation of the conjugate gradient merhod for...

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18th International Conference on Geoinformatics: Theoretical and Applied Aspects, Geoinformatics 2019

作者： Akimova, E.N. Misilov, V.E. Sultanov, M.A. Ural Federal University Krasovskii Institute of Mathematics and Mechanics UrB RAS Ekaterinburg Russia Akhmet Yassawi International Kazakh-Turkish University Turkistan Kazakhstan

ISBN: (纸本)9781510889675

The paper is devoted to development and implementation of efficient parallel algorithm based on the conjugate gradient method for solving the nonlinear inverse potential problem of finding a boundary surface in two-layer medium. The algorithm uses the approximation of the Jacobian matrix of the integral operator based on dropping out the lesser elements and utilizing the Toeplitz-block-Toeplitz structure of the matrix. The parallel algorithm was implemented for the graphics processor using CUDA technology. The numerical experiments was carried out. Investigation of efficiency and speedup of the parallel algorithm was performed. © EAGE 2019.

关键词： parallel algorithms

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Generating Minimal Nondeterministic Finite Automata Using a parallel Algorithm

Generating Minimal Nondeterministic Finite Automata Using a ...

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International Symposium on parallel and Distributed Computing

作者： Tomasz Jastrzab Zbigniew J. Czech Wojciech Wieczorek Dept. of Algorithmics and Software Silesian University of Technology Gliwice Poland Faculty of Science and Technology Silesia University in Katowice Katowice Poland

ISBN: (数字)9781728189468

ISBN: (纸本)9781728189475

The goal of this paper is to develop a parallel algorithm that, on input of a learning sample, identifies a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA or the range of possible sizes of such an NFA, that represents the target regular language is sought. We define the task of finding an NFA, which accepts all positive examples and rejects all negative ones, as a constraint satisfaction problem, and then propose a parallel algorithm to solve the problem. The results of computational experiments on the variety of test samples are reported.

关键词： Learning automata Task analysis Inference algorithms Automata parallel algorithms Software algorithms Software

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parallel algorithm for prediction of variables in Simultaneous Equation Models

Parallel algorithm for prediction of variables in Simultaneo...

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2019 International Conference on High Performance Computing and Simulation, HPCS 2019

作者： Gomez, Oscar Lopez-Espin, Jose J. Penalver, Antonio Miguel Hernández University Center of Operations Research Elche Spain

ISBN: (纸本)9781728144849

Simultaneous equation models (SEM) are multivariate techniques that reflect the presence of jointly endogenous variables. Traditionally, these models have been used in economy, expanding in last decades into other disciplines. One of usefulness of the SEM is the future estimation of the endogenous variables once the coefficient of the model has been obtained. This estimation is made using the actual information of endogenous and exogenous variables, as well as the matrices of the model. This work studies a parallel algorithm for the future prediction of the endogenous variables of an SEM model. Experimental tests comparing shared memory and message passing algorithms are made when varying the problem size, in order to check the behaviour of the algorithm and the ideal resources to use. © 2019 IEEE.

关键词： parallel algorithms

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A parallel algorithm for the simulation of controlled motion of space tether systems 5

A parallel algorithm for the simulation of controlled motion...

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5th International Conference on Information Technology and Nanotechnology, ITNT 2019

作者： Zhidchenko, V.V. Kovartsev, A.N. Samara National Research University Moskovskoe Shosse 34 uni0410 Samara443086 Russia

Simulation of the controlled motion of space tether systems differs significantly from the free motion case. Tether deployment increases its length and adds new points into the discrete model of the tether. A set of dynamic equations is expanded with equations that describe the motion of the new points. The necessary condition at each step is the consistency of the system. Tether deployment is supervised by the control system which limits the tether length. The problem arises of solving a system of differential equations with variable and increasing number of equations. In this paper a parallel algorithm for simulating the controlled motion of space tether systems is designed. The parallel algorithm provides an ability to split the tether to such number of points that the model becomes similar to the model with distributed parameters. As a result, the simulation of several modes of tether motion unavailable for other methods becomes possible. The estimation of speedup provided by the algorithm is implemented. It shows that the speedup is comparable with the more simple case of the free motion simulation. Computational experiments have been carried out for the algorithm performance evaluation. © Published under licence by IOP Publishing Ltd.

关键词： parallel algorithms

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parallel dimensionality reduction for multiextremal optimization problems 1

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15th International Conference on parallel Computing Technologies, PaCT 2019

作者： Gergel, Victor Grishagin, Vladimir Israfilov, Ruslan Lobachevsky State University Gagarin Avenue 23 Nizhni Novgorod603950 Russia

ISBN: (数字)9783030256364

ISBN: (纸本)9783030256357

The paper is devoted to consideration of numerical global optimization methods in the framework of the approach of reducing dimensionality based on nested optimization schemes. For the adaptive nested scheme being more efficient in comparison with its classical prototype a new algorithm of parallel implementation is proposed. General descriptions of the parallel techniques both for synchronous and asynchronous versions are given. Results of numerical experiments on a set of complicated multiextremal test problems of high dimension are presented. These results demonstrate essential acceleration of asynchronous parallel algorithm in comparison with the sequential version. © 2019, Springer Nature Switzerland AG.

关键词： parallel algorithms

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Monte-Carlo simulation based multi-objective optimum allocation of renewable distributed generation using OpenCL

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ELECTRIC POWER SYSTEMS RESEARCH 2019年第May期170卷 81-91页

作者： Abdelaziz, Morad Moradzadeh, Majid Univ British Columbia Sch Engn Fac Appl Sci Kelowna BC Canada

Monte-Carlo simulation (MCS) is the most accurate technique for considering the stochastic nature of renewable energy resources in power system analysis and planning. However, due to its heavy computational burden, MCS is rarely utilized when solving the multi-objective renewable distributed generation (DG) allocation problem. In order to address this problem, this paper proposes a novel methodology to exploit the massively parallel architecture of graphics processing units (GPU) in a way that enables the use of MCS when solving the multi-objective renewable DG allocation problem. First, the renewable DG allocation problem is formulated as a multi-objective optimization problem to minimize the lines losses and the costs pertaining to installing renewable DG units in the distribution network. Then, a parallelized implementation of NSGA-II using OpenCL is described in details to solve the formulated multi-objective renewable DG planning problem. The feasibility and effectiveness of the proposed methodology are validated using the IEEE 32-bus test system and two real distribution test systems. The results show that the proposed parallelized implementation can enable the use of MCS for modelling the generation and demand uncertainties when solving the multi-objective renewable DG allocation problem using a metaheuristic approach.

关键词： Distribution system High performance computing (HPC) Multi-objective programming Renewable energy resources parallel algorithms Uncertainties modeling

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An efficient parallel algorithm for determining flow directions over flat surfaces based on raster digital elevation models

An efficient parallel algorithm for determining flow directi...

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International Conference on Information Science and Control Engineering (ICISCE)

作者： Lihui Song Guiyun Zhou Yang Chen Zhonghua Su School of Resources and Environment University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical Science Beijing Normal University Beijing China

The determination of flow directions is an essential step for drainage network extraction, and flat surfaces are common features in flow direction determination. With the challenge of a massive volume of digital elevation models (DEMs), to reduce the running time and memory usage, there is a growing need to develop parallel algorithms to calculate flow directions over flat surfaces. We propose an efficient parallel algorithm for flow directions over flat surfaces based on the existing serial algorithm and three-step parallel framework. The proposed algorithm assigns pre-divided tiles to consumer processes to build local graphs. Then the producer process builds global graphs based on all the local graphs. Finally, consumer processes update the local graphs based on the global graphs and determine flow directions over flat surfaces. For all tested DEMs, the speed-up ratios are greater than 5 with 11 consumer processes. The strong scaling efficiencies are greater than 40% with 11 consumer processes. The proposed algorithm can run generally faster, use less memory, and process massive DEMs that cannot be successfully processed using the existing serial algorithm. This study shows that the proposed algorithm is an ideal parallel algorithm for determining flow direction over flat surfaces in massive DEMs.

关键词： Information science Control engineering Memory management Feature extraction parallel algorithms Surface treatment Digital elevation models

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A High-Performance parallel Approach to Image Processing in Distributed Computing

A High-Performance Parallel Approach to Image Processing in ...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Mekhriddin Rakhimov Doniyor Mamadjanov Abulkosim Mukhiddinov Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9781728173863

ISBN: (纸本)9781728173870

Digital image processing is an actual task in the digital communication systems, IP-telephony and video conferencing, in digital television, and video surveillance. Digital processing of large video images takes a lot of time, especially if it happens in a real-time system. And, processing speed plays an important role in recognition of objects in video images received from IP-cameras in real time. This requires the use of modern technologies, and fast algorithms that increase the acceleration of digital image processing. Acceleration problems have not been fully resolved till present. Today's realities are such that the development of accelerated image processing programs requires a good knowledge of parallel and distributed computing. Both of these areas are united by the fact that both parallel and distributed software consists of several processes that together solve one common problem. This article proposes an accelerated method for the tasks of recognizing objects in video images received from IP-cameras using parallel and distributed computing technologies.

关键词： Streaming media Video surveillance Real-time systems Acceleration Distributed computing Task analysis parallel algorithms

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