scholarly journals An adaptive parallel algorithm for finite language decomposition

2021 ◽  
Author(s):  
Tomasz Jastrzęb ◽  
Zbigniew J. Czech ◽  
Wojciech Wieczorek
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Search Space ◽  
Exhaustive Search ◽  
Computational Experiments ◽  
Hard Problem ◽  
Finite Language ◽  
Behavior Based

AbstractThe computationally hard problem of finite language decomposition is investigated. A finite language L is decomposable if there are two languages L1 and L2 such that L = L1L2. Otherwise, L is prime. The main contribution of the paper is an adaptive parallel algorithm for finding all decompositions L1L2 of L. The algorithm is based on an exhaustive search and incorporates several original methods for pruning the search space. Moreover, the algorithm is adaptive since it changes its behavior based on the runtime acquired data related to its performance. Comprehensive computational experiments on more than 4000 benchmark languages generated over alphabets of various sizes have been carried out. The experiments showed that by using the power of parallel computing the decompositions of languages containing more than 200000 words can be found. Decompositions of languages of that size have not been reported in the literature so far.

PARALLEL COMPUTING OF NUMERICAL SCHEMES AND BIG DATA ANALYTIC FOR SOLVING REAL LIFE APPLICATIONS

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Norma Alias ◽  
Nadia Nofri Yeni Suhari ◽  
Hafizah Farhah Saipan Saipol ◽  
Abdullah Aysh Dahawi ◽  
Masyitah Mohd Saidi ◽  
...  
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Parallel Algorithm ◽  
Sparse Matrices ◽  
Real Life ◽  
Poor Performance ◽  
Equation System ◽  
Numerical Schemes ◽  
Linear Equation System ◽  
Data Analytic

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance.

scholarly journals Parallel Strategy to Factorize Fermat Numbers with Implementation in Maple Software

2021 ◽  
pp. 167-173
Author(s):  
Jianhui Li ◽  
◽  
Manlan Liu
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Computational Efficiency ◽  
Sequential Algorithm ◽  
Parallel Processes ◽  
Fermat Numbers ◽  
Fermat Number ◽  
Parallel Strategy

In accordance with the traits of parallel computing, the paper proposes a parallel algorithm to factorize the Fermat numbers through parallelization of a sequential algorithm. The kernel work to parallelize a sequential algorithm is presented by subdividing the computing interval into subintervals that are assigned to the parallel processes to perform the parallel computing. Maple experiments show that the parallelization increases the computational efficiency of factoring the Fermat numbers, especially to the Fermat number with big divisors.

scholarly journals A Bounded Exhaustive Search Technique for Optimal Phasor Measurement Unit Placement in Power Grids

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2021
Author(s):  
Ahmad Asrul Ibrahim ◽  
Khairuddin Khalid ◽  
Hussain Shareef ◽  
Nor Azwan Mohamed Kamari
Keyword(s):  
Search Space ◽  
Exhaustive Search ◽  
Operating Conditions ◽  
Power Grids ◽  
Phasor Measurement Unit ◽  
Computational Time ◽  
Measurement Unit ◽  
Search Technique ◽  
Phasor Measurement ◽  
Pmu Placement

This paper proposes a technique to determine the possible optimal placement of the phasor measurement unit (PMU) in power grids for normal operating conditions. All possible combinations of PMU placement, including infeasible combinations, are typically considered in finding the optimal solution, which could be a massive search space. An integer search algorithm called the bounded search technique is introduced to reduce the search space in solving a minimum number of PMU allocations whilst maintaining full system observability. The proposed technique is based on connectivity and symmetry constraints that can be derived from the observability matrix. As the technique is coupled with the exhaustive technique, the technique is called the bounded exhaustive search (BES) technique. Several IEEE test systems, namely, IEEE 9-bus, IEEE 14-bus, IEEE 24-bus and IEEE 30-bus, are considered to showcase the performance of the proposed technique. An initial Monte Carlo simulation was carried out to evaluate the capability of the bounded search technique in providing a smaller feasible search space. The effectiveness of the BES technique in terms of computational time is compared with the existing exhaustive technique. Results demonstrate that the search space can be reduced tremendously, and the computational burden can be eased, when finding the optimal PMU placement in power grids.

A TECHNIQUE FOR EXACT COMPUTATION OF PRECOLORING EXTENSION ON INTERVAL GRAPHS

2013 ◽  
Vol 24 (01) ◽  
pp. 109-122
Author(s):  
MARTIN R. EHMSEN ◽  
KIM S. LARSEN
Keyword(s):  
Real Life ◽  
Computation Time ◽  
Interval Graph ◽  
Interval Graphs ◽  
Exhaustive Search ◽  
Hard Problem ◽  
Proper Coloring ◽  
Exact Computation ◽  
New Approach ◽  
Reasonable Time

Inspired by a real-life application, we investigate the computationally hard problem of extending a precoloring of an interval graph to a proper coloring under some bound on the number of available colors. We are interested in quickly determining whether or not such an extension exists on instances occurring in practice in connection with campsite bookings on a campground. A naive exhaustive search does not terminate in reasonable time. We have formulated a new approach which moves the computation time within the usable range on all the data samples available to us.

A cooperative search for berth scheduling

2016 ◽  
Vol 31 (5) ◽  
pp. 498-507 ◽  
Author(s):  
Eduardo Lalla-Ruiz ◽  
Belén Melián-Batista ◽  
José Marcos Moreno-Vega
Keyword(s):  
Container Terminal ◽  
Search Space ◽  
Computational Experiments ◽  
Berth Allocation ◽  
Cooperative Search ◽  
Berth Allocation Problem ◽  
High Quality ◽  
Grouping Strategy ◽  
Relevant Role ◽  
Berth Scheduling

AbstractWith the growing demand of freight transport by means of container vessels as well as the important competition among terminals, managers and stakeholders seek to improve the exploitation of the container terminal resources efficiently. In this context, arises the Berth Allocation Problem, which aims to allocate and schedule incoming vessels along the quay. Its appropriate solution plays a relevant role in enhancing the terminal productivity. Thus, for addressing this problem, we propose a cooperative search, where the individuals are organized into groups and each member shares information with its group partners. This grouping strategy allows to diversify as well as intensify the search in some regions by means of information shared among the individuals of each group. The computational experiments for this problem reveal that our approach reports high-quality solutions and identifies promising regions within the search space in short computational times.

scholarly journals Problem-oriented software package for the numerical solution and research of structural bioinformatics problems using stochastic optimization methods

2020 ◽  
pp. 37-47
Author(s):  
S. Poluyan ◽  
N. Ershov
Keyword(s):  
Stochastic Optimization ◽  
Open Source Software ◽  
Software Package ◽  
Structure Prediction ◽  
Optimization Methods ◽  
Search Space ◽  
Protein Docking ◽  
Structural Bioinformatics ◽  
Computational Experiments ◽  
Continuous Global Optimization

In this paper presented problem-oriented software package for performing computational experiments in structural bioinformatics problems: protein structure prediction and peptide-protein docking. These problemsare formulated as continuous global optimization tasks. The primary purpose of the presented software package is to provide functionality for performing computational experiments using various stochastic optimization methods. To perform experiments for the selected task the objective function and search space are provided for user. In this work the software packagefunctionality, implementation features and the results of various experimentsare presented. The software is written in C++ and provides the possibility ofusing parallel computing using OpenMP technology. The presented package is open source software that stored in the GitHub repositories.

scholarly journals Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianqi Lai ◽  
Hang Yu ◽  
Zhengyu Tian ◽  
Hua Li
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Graphics Processing Units ◽  
Message Passing Interface ◽  
Gpu Computing ◽  
Programming Model ◽  
Domain Decomposition Method ◽  
Parallel Execution ◽  
Performance Measurements ◽  
Cfd Applications

Graphics processing units (GPUs) have a strong floating-point capability and a high memory bandwidth in data parallelism and have been widely used in high-performance computing (HPC). Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for the GPU to reduce the complexity of programming. The programmable GPUs are becoming popular in computational fluid dynamics (CFD) applications. In this work, we propose a hybrid parallel algorithm of the message passing interface and CUDA for CFD applications on multi-GPU HPC clusters. The AUSM + UP upwind scheme and the three-step Runge–Kutta method are used for spatial discretization and time discretization, respectively. The turbulent solution is solved by the K−ω SST two-equation model. The CPU only manages the execution of the GPU and communication, and the GPU is responsible for data processing. Parallel execution and memory access optimizations are used to optimize the GPU-based CFD codes. We propose a nonblocking communication method to fully overlap GPU computing, CPU_CPU communication, and CPU_GPU data transfer by creating two CUDA streams. Furthermore, the one-dimensional domain decomposition method is used to balance the workload among GPUs. Finally, we evaluate the hybrid parallel algorithm with the compressible turbulent flow over a flat plate. The performance of a single GPU implementation and the scalability of multi-GPU clusters are discussed. Performance measurements show that multi-GPU parallelization can achieve a speedup of more than 36 times with respect to CPU-based parallel computing, and the parallel algorithm has good scalability.

Parallel Computing for Mining Association Rules in Distributed P2P Networks

2011 ◽  
pp. 47-62
Author(s):  
Huiwei Guan
Keyword(s):  
Data Mining ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Parallel Algorithm ◽  
Association Rules ◽  
Distributed Databases ◽  
Computing System ◽  
Computing Systems ◽  
P2p Computing ◽  
P2p Systems

Distributed computing and Peer-to-Peer (P2P) systems have emerged as an active research field that combines techniques which cover networks, distributed computing, distributed database, and the various distributed applications. Distributed Computing and P2P systems realize information systems that scale to voluminous information on very large numbers of participating nodes. Data mining on large distributed databases is a very important research area. Recently, most work for mining association rules focused on a single machine or client-server network model. However, this traditional approach does not satisfy the requirements from the large distributed databases and applications in a P2P computing system. Two important challenges are raised, one is how to implement data mining for large distributed databases in P2P computing systems, and the other is how to develop parallel data mining algorithms and tools for the distributed P2P computing systems to improve the efficiency. In this chapter, a parallel association rule mining approach in a P2P computing system is designed and implemented, which satisfies the distribution of the P2P computing system well and makes parallel computing become true. The performance and comparison of the parallel algorithm with the sequential algorithm is analyzed and evaluated, which presents the parallel algorithm features consistent implementation, higher performance, and fine scalable ability.

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