Master-slave Model Research Articles

Self-Stabilizing Master-Slave Token Circulation Algorithm in Undirected Rings and Unicyclic Graphs of Arbitrary Size and Their Orientations

Token circulation is a fundamental task in the distributed systems. In this paper, we propose a constant space randomized self-stabilizing master-slave token circulation algorithm that works for undirected rings and undirected unicyclic graphs of arbitrary size. We consider the recently introduced and studied master-slave model where a single node is designated to be a master node and other nodes are anonymous slave nodes. The expected stabilization time is O ( n log n ) steps, and the space requirement at each node is 4 bits for any undirected ring (or unicyclic graph) of size n under an unfair distributed daemon; the nodes do not need the knowledge of the size of the ring and hence the protocol is suited for dynamic graphs. The proposed token circulation algorithm is further extended to achieve orientation in the ring and the unicyclic graph. Disregarding the time for stabilization, the orientation can be done in at most O ( n ) steps with 1 bit extra storage at each node for the ring and the unicyclic graph.Â

A Master-Slave Model NGA and its Application in the Multidimensional 0-1 Knapsack Problem

The Multidimensional 0-1 knapsack problem is a NP hard problem, though there are many algorithm is used to solve the problem, but there is still not a good solution to solving the problem. This paper improved niche genetic algorithm, established a master-slave mode niche genetic algorithm, and carried on adaptive setting the individual Euclidean distance criterion, making it can changed with the evolving algebra incremental. At last, used master-slave niche genetic algorithm to solve the Multidimensional 0-1 knapsack problem, test results showed, the algorithm has good applicability and superiority in solving the Multidimensional 0-1 knapsack problem.

A parallel system for adaptive optics based on parallel mutation PSO algorithm

Wavefront sensor-less adaptive optics technology has a much higher requirement for the processing speed than ever before. To improve the efficiency of particle swarm optimization (PSO), this paper proposes a novel parallel mutation particle swarm optimization (PMPSO) algorithm based on a master-slave model. The parallel system based on PMPSO algorithm is applied in a 61-element adaptive optics system to control the deformable mirror. The experimental results show that the parallel system based on PMPSO algorithm has a rapid convergence speed, which can effectively correct the wavefront aberration.

Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data

The imperfect sequence data produced by next-generation sequencing technologies have motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures. In this article, we present Musket, an efficient multistage k-mer-based corrector for Illumina short-read data. We use the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared with all other evaluated correctors as well as a highly competitive overall execution time. Musket is available at http://musket.sourceforge.net.

A Hybrid Double-Layer Master-Slave Model For Multicore-Node Clusters

The Double-Layer Master-Slave Model (DMSM) is a suitable hybrid model for executing a workload that consists of multiple independent tasks of varying length on a cluster consisting of multicore nodes. In this model, groups of individual tasks are first deployed to the cluster nodes through an MPI based Master-Slave model. Then, each group is processed by multiple threads on the node through an OpenMP based All-Slave approach. The lack of thread safety of most MPI libraries has to be addressed by a judicious use of OpenMP critical regions and locks.The HPCVL DMSM Library implements this model in Fortran and C. It requires a minimum of user input to set up the framework for the model and to define the individual tasks. Optionally, it supports the dynamic distribution of task-related data and the collection of results at runtime. This library is freely available as source code. Here, we outline the working principles of the library and on a few examples demonstrate its capability to efficiently distribute a workload on a distributed-memory cluster with shared-memory nodes.

An Efficient Parallel Algorithm for Mining Frequent Pattern

Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. An Efficient Parallel algorithm for Mining frequent pattern (EPM) was proposed and Fast Distributed association rules Mining (FDM) algorithm was improved. Hash table technology was used to improve the generation efficiency of the 2nd candidate items . It also reduces the number of transactions in transaction database using Tid table technology. A master-slave model of parallel algorithm for mining association rules is designed in the algorithm to reduce the communication cost. The experimental results show that this algorithm has a high efficiency to deal with large database.

Parallel cooperative micro-particle swarm optimization: A master–slave model

A parallel master–slave model of the recently proposed cooperative micro-particle swarm optimization approach is introduced. The algorithm is based on the decomposition of the original search space in subspaces of smaller dimension. Each subspace is probed by a subswarm of small size that identifies suboptimal partial solution components. A context vector that serves as repository for the best attained partial solutions of all subswarms is used for the evaluation of the particles. The required modifications to fit the original algorithm within a parallel computation framework are discussed along with their impact on performance. Also, both linear and random allocation of direction components to subswarms are considered to render the algorithm capable of capturing possible correlations among decision variables. The proposed approach is evaluated on two types of computer systems, namely an academic cluster and a desktop multicore system, using a popular test suite. Statistical analysis of the obtained results reveals that, besides the expected run-time superiority of the parallel model, significant improvements in solution quality can also be achieved. Different factors that may affect performance are pointed out, offering intuition on the expected behavior of the parallel model.

An analysis of super-linear speedup for master-slave model

An analysis of super-linear speedup for master-slave model

Optimum design of high-rise steel buildings using an evolution strategy integrated parallel algorithm

The present study addresses a parallel solution algorithm for optimum design of large steel space frame structures, in particular high-rise steel buildings. The algorithm implements a novel discrete evolution strategy optimization method to effectively size these systems for minimum weight according to the provisions of ASD-AISC specification and various practical aspects of design process. The multitasking environment in the algorithm rests on a master–slave model based parallelization of the optimization procedure, which provides an ideal platform for attaining optimal solutions in a timely manner without losing accuracy in computations. Three design examples from the category of high-rise steel buildings are studied extensively to demonstrate cost-efficiency of the algorithm in conjunction with a cluster of computers with 32 processors. The variation in performance of the parallel computing system with respect to the number of processors employed is also scrutinized in each design example.

A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.

A heterogeneous cooperative parallel search of branch-and-bound method and tabu search algorithm

In this study, we present a heterogeneous cooperative parallel search that integrates branch-and-bound method and tabu search algorithm. These two algorithms perform searches in parallel and cooperate by asynchronously exchanging information about the best solutions found and new initial solutions for tabu search. The rapid production of a good solution from the tabu search process provides the branch-and-bound process with a better feasible solution to accelerate the elimination of subproblems that do not contain an optimal solution. The new initial solution produced from the subproblem with a least-cost lower bound of the branch-and-bound method suggests the best potential area for tabu search to explore. We use a master-slave model to reduce the complexity of communication and enhance the performance of data exchange. A branch-and-bound process is used as the master process to control the exchange of information and the termination of computation. Several tabu search processes are executed simultaneously as the slave processes and cooperate by asynchronously exchanging information on the best solutions found and the new initial solutions by the master process of branch-and-bound. Based on the computation experiments of solving traveling salesman problems (TSP), the proposed heterogeneous parallel search algorithm outperforms a conventional parallel branch-and-bound method and a conventional parallel tabu search. We also present the computational results showing the efficiency of heterogeneous cooperative parallel search when we use more processors to accelerate search time. Thus, the proposed heterogeneous parallel search algorithm achieves linear accelerations.

Solution of molecule scattering model based on parallel mode

The solution of Electron-molecule scattering model based on serial mode meets two challenges: long running time and large memory requirement.Parallel computing which would reduce the workload of one computer is a useful method to meet the challenge.Based on master-slave parallel model structure,several optimized approaches were introduced and the parallel algorithm for Electron-molecule scattering was realized.Results of the experiments obtain satisfied speedup and parallel efficiency,and prove the feasibility and validity of this solution.

Multiprocessor modeling of parallel Particle Swarm Optimization applied to nuclear engineering problems

Particle Swarm Optimization (PSO) is a population-based metaheuristic (PBM), in which solution candidates evolve through simulation of a simplified social adaptation model. Putting together robustness, efficiency and simplicity, PSO has gained great popularity. Many successful applications of PSO are reported, in which PSO demonstrated to have advantages over other well-established PBM. However, computational costs are still a great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time consuming objective functions. To overcome such difficulty, parallel computation has been used. The default advantage of parallel PSO (PPSO) is the reduction of computational time. Master-slave approaches, exploring this characteristic are the most investigated. However, much more should be expected. It is known that PSO may be improved by more elaborated neighborhood topologies. Hence, in this work, we develop several different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by communication strategies in multiprocessor architectures. The proposed PPSOs have been applied to two complex and time consuming nuclear engineering problems: i) reactor core design (CD) and ii) fuel reload (FR) optimization. After exhaustive experiments, it has been concluded that: i) PPSO still improves solutions after many thousands of iterations, making prohibitive the efficient use of serial (non-parallel) PSO in such kind of real-world problems and ii) PPSO with more elaborated communication strategies demonstrated to be more efficient and robust than the master-slave model. Advantages and peculiarities of each model are carefully discussed in this work.

Aerodynamic optimization using a parallel asynchronous evolutionary algorithm controlled by strongly interacting demes

A parallel asynchronous evolutionary algorithm controlled by strongly interacting demes for single- and multi-objective optimization problems is proposed. It is suitable even for non-homogeneous, multiprocessor systems, ensuring maximum exploitation of the available processors. The search algorithm utilizes a structured topology of evaluation agents organized in a number of inter-communicating demes arranged on a 2D supporting mesh. Once an evaluation terminates and a processor becomes idle, a series of intra- and inter-deme processes determines the next agent to undergo evaluation on this specific processor. Real coding and differential evolution operators are used. Mathematical and aerodynamic-turbomachinery optimization problems are presented to assess the proposed method in terms of CPU cost, parallel efficiency and quality of solutions obtained within a predefined number of evaluations. Comparisons with conventional evolutionary algorithms, parallelized based on the master–slave model on the same computational platform, are presented.

Solving the Quadratic Assignment Problems using Parallel ACO with Symmetric Multi Processing

In this paper, we propose several types of parallel ant colony optimization algorithms with symmetric multi processing for solving the quadratic assignment problem (QAP). These models include the master-slave models and the island models. As a base ant colony optimization algorithm, we used the cunning Ant System (cAS) which showed promising performance our in previous studies. We evaluated each parallel algorithm with a condition that the run time for each parallel algorithm and the base sequential algorithm are the same. The results suggest that using the master-slave model with increased iteration of ant colony optimization algorithms is promising in solving quadratic assignment problems for real or real-like instances.

Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization

A Grid-enabled optimization environment is presented. It is based on Metamodel-Assisted Evolutionary Algorithms ( MAEAs), where radial basis function networks, trained on the fly on selected subsets of the previously evaluated individuals, are used to pre-evaluate the population members. The search follows a Hierarchical and Distributed scheme ( HDMAEA), with more than one search level, each of which is associated with a different problem-specific evaluation tool and a different number of semi-isolated demes. Irrespective of the use of cluster or Grid computing, the HDMAEA drastically reduces the number of evaluations required to reach the optimal solution(s). The Grid-enabled HDMAEA, based on the master–slave model with simultaneously evaluated population members, aims at solving large scale optimization problems in affordable wall clock time. In the proposed Grid–computing setup, Condor is used as the local resource manager on each contributing cluster, authentication and interfacing is carried out via the Globus Toolkit and the unification of Grid resources under a common queue is undertaken by the Gridway metascheduler. If more than one search level are used (hierarchical search), the optimization of Grid resources’ allocation relies on the distinction between computationally demanding, high-accuracy and less demanding, low-accuracy evaluation tools. The proposed Grid-enabled problem solving environment is demonstrated on three aerodynamic shape optimization problems, namely the design of a compressor cascade and two 3D elbow ducts, on three remote clusters.

An optimised simulation of component-based embedded systems in manufacturing industry

This paper deals with an optimised simulation of embedded systems following the international standard IEC61499 in which a Function Block is an event triggered component owning data and an application is a network of blocks that have to satisfy properties described in user requirements. We design these components in different abstraction levels of a hierarchy to control the development complexity. To achieve a feasible simulation, we formalise at first-time the FB concept by defining the Control Function Module (CFM) and the Virtual Plant Module (VPM). We propose thereafter a technique processing temporal deadlines for control modules and we define in the system behaviour critical scenarios where properties can be violated. We apply the simulation around these scenarios by applying a fault injection technique to optimise the system validation and to consider its hierarchy, we propose a simulator architecture based on the master-slave model where a slave injects faults in each level of the hierarchy, whereas the master chooses the best values of these errors to generate critical scenarios.

Scheduling problems in master-slave model

We consider scheduling problems in the master slave model, which was introduced by Sahni in 1996. The goal is to minimize the makespan and the total completion time. It has been shown that the problem of minimizing makespan is NP-hard. Sahni and Vairaktarakis developed some approximation algorithms to generate schedules whose makespan is at most constant times the optimal. In this paper, we show that the problem of minimizing total completion time is NP-hard in the strong sense. Then we develop algorithms to generate schedules whose total completion time and makespan are both bounded by some constants times their optimal values.

Architectural Exploration of Heterogeneous Multiprocessor Systems for JPEG

Multicore processors have been utilized in embedded systems and general computing applications for some time. However, these multicore chips execute multiple applications concurrently, with each core carrying out a particular task in the system. Such systems can be found in gaming, automotive real-time systems and video / image encoding devices. These system are commonly deployed to overcome deadline misses, which are primarily due to overloading of a single multitasking core. In this paper, we explore the use of multiple cores for a single application, as opposed to multiple applications executing in a parallel fashion. A single application is parallelized using two different methods: one, a master-slave model; and two, a sequential pipeline model. The systems were implemented using Tensilica's Xtensa LX processors with queues as the means of communications between two cores. In a master-slave model, we utilized a course grained approach whereby a main core distributes the workload to the remaining cores and reads the processed data before writing the results back to file. In the pipeline model, a lower granularity is used. The application is partitioned into multiple sequential blocks; each block representing a stage in a sequential pipeline. For both models we applied a number of differing configurations ranging from a single core to a nine-core system. We found that without any optimization for the seven core system, the sequential pipeline approach has a more efficient area usage, with an area increase to speedup ratio of 1.83 compared to the master-slave approach of 4.34. With selective optimization in the pipeline approach, we obtained speed ups of up to 4.6× while with an area increase of only 3.1× (area increase to speedup ratio of just 0.68).

A self‐distributing software environment for immersive multiprojector displays

AbstractIn multiprojector displays which surround users with high‐resolution images, a PC‐Cluster is often used for realistic and real‐time image generation. However, it is quite troublesome to develop applications which support parallel processing on the PC‐Cluster. It is also difficult to acquire enough rendering performance because of the limited bandwidth of the PC‐Cluster. Therefore, we aim to achieve affordable and accessible software environments for the multiprojector displays. In this paper, we describe a self‐distributing software environment for inheriting existent game engines which provide basic functions of realizing virtual environments. This environment achieves minimum data communication based on a master–slave model. The communication mechanism is automatically applied to target applications by intercepting APIs. Hence, we can directly exploit high capability of the existing game engines on the multiprojector displays. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(2): 1–9, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20705