This paper presents a parallel algorithm for computing fixpoints of Galois connections induced by object-attribute relational data. The algorithm results as a parallelization of CbO (Kuznetsov 1999) in which we proces...
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This paper presents a parallel algorithm for computing fixpoints of Galois connections induced by object-attribute relational data. The algorithm results as a parallelization of CbO (Kuznetsov 1999) in which we process disjoint sets of fixpoints simultaneously. One of the distinctive features of the algorithm compared to other parallel algorithms is that it avoids synchronization which has positive impacts on its speed and implementation. We describe the parallel algorithm, prove its correctness, and analyze its asymptotic complexity. Furthermore, we focus on implementation issues, scalability of the algorithm, and provide an evaluation of its efficiency on various data sets.
A parallel algorithm is presented for generating n! permutations of n given items. The algorithm requires 0(n!) time steps and runs on a linear array in which each processing element (PE) contains only constant stora...
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A parallel algorithm is presented for generating n! permutations of n given items. The algorithm requires 0(n!) time steps and runs on a linear array in which each processing element (PE) contains only constant storage size and the elapsed time of a time step is independent of n. The basic idea for designing the algorithm is the use of iterative method and adding modulo operation. The essential part in the algorithm is that, during the execution of current cycle-n-permutations with a header, the next header for the next cycle-n-permutations must be prepared in time. In the linear array, since each PE is identical and executes the same program, it is suitable for very large-scale integration implementation.
In order to solve the problems of traditional harmony search in complex function multiobjective optimization, such as low precision, slow convergence, and easy to fall into local optimum, this article proposes a multi...
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In order to solve the problems of traditional harmony search in complex function multiobjective optimization, such as low precision, slow convergence, and easy to fall into local optimum, this article proposes a multiobjective optimization harmony search parallel algorithm based on cloud computing. First, according to the characteristics that the traditional harmony search algorithm uses a single harmony library for storing and processing the memory harmony, and it is divided into multiple harmony sublibraries according to different harmony. At the same time, the roulette selection and dynamic trade-off factor strategies are used for the dynamic setting of harmony memory library value-taking probability, pitch fine-tuning probability, pitch fine-tuning bandwidth, and other parameters which the traditional harmony search algorithm mainly relies on. Then, MapReduce programming model is used to establish Map and Reduce core parallel computing functions, to construct the parallel algorithm of dynamic parameter harmony search based on cloud computing. Finally, the algorithm optimization comparison test is conducted on Hadoop platform and compared with several existing optimal harmony search algorithms, the searching precision of this algorithm is improved by eight orders of magnitude, and the iteration number on the convergence speed is reduced by 6500 times, and the parallel achieves the linear acceleration ratio. Experimental results show that the optimization efficiency of this algorithm is higher than several existing optimal harmony search algorithms.
High performance computing has opened the door to using bioinformatics and systems biology to explore complex relationships among data, and created the opportunity to tackle very large and involved simulations of biol...
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High performance computing has opened the door to using bioinformatics and systems biology to explore complex relationships among data, and created the opportunity to tackle very large and involved simulations of biological systems. Many supercomputing centers have jumped on the bandwagon because the opportunities for significant impact in this field is infinite. Development of new algorithms, especially parallel algorithms and software to mine new biological information and to assess different relationships among the members of a large biological data set, is becoming very important. This article presents our work on the design and development of parallel algorithms and software to solve some important open problems arising from bioinformatics, such as structure alignment of RNA sequences, finding new genes, alternative splicing, gene expression clustering and so on. In order to make these parallel software available to a wide audience, the grid computing service interfaces to these software have been deployed in China National Grid (CNGrid). Finally, conclusions and some future research directions are presented.
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 w...
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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.
parallel algorithms are problematic to develop because of the negative influence of synchronisation, complicated behaviour of threads' capturing computing resources. Experimental results show performance time'...
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parallel algorithms are problematic to develop because of the negative influence of synchronisation, complicated behaviour of threads' capturing computing resources. Experimental results show performance time's strong dependence on algorithm parameters, such as the number of subtasks and the complexity of each task. The optimal value of subtask complexity is revealed for the particular algorithm. It is the same for different complexity of the parallelised task (with the same computing resource). To guarantee algorithm speed-up it is important to have a method for investigating the efficiency of parallel algorithm before its implementation on specified computing resources. Stochastic Petri net potentially could be a high accuracy tool for investigating the efficiency of a parallel algorithm. However, a huge number of elements are needed to compose a model of non-trivial algorithm that limits the application of this tool in practice. Petri-object simulation method allows replication of Petri nets with specified parameters and model creation of a list of linked Petri-objects. Basic templates for the model creation of a multithreaded algorithm are developed. Applying these templates, the model of the parallel discrete event simulation algorithm is developed and investigated. By the model results, the algorithm parameters providing the least performance time can be determined.
In Surface wave waveform inversion, we want to reconstruct 3D shear wave velocity structure, which calculation beyond the capability of the powerful present day personal computer or even workstation. So we designed a ...
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In Surface wave waveform inversion, we want to reconstruct 3D shear wave velocity structure, which calculation beyond the capability of the powerful present day personal computer or even workstation. So we designed a high paralleled algorithm and carried out the inversion on parallel computer based on the partitioned waveform inversion (PWI). It partitions the large scale optimization problem into a number of independent small scale problems and reduces the computational effort by several orders of magnitude. We adopted surface waveform inversion with a equal block(2 o×2 o) discretization.
Analysis of long time series is relevant in many current applications. These investigations are usually carried out on multiprocessor computers (supercomputers). However, supercomputer calculations are efficient only ...
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The parallel algorithm of Petri net based on multicore clusters is put forward in order to make the Petri net system with concurrent synchronous function realize parallel control and running. First, select different P...
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ISBN:
(纸本)9781467365932
The parallel algorithm of Petri net based on multicore clusters is put forward in order to make the Petri net system with concurrent synchronous function realize parallel control and running. First, select different Petri net structures and conduct transformation, and give the partitioning method of the subnets of place invariant-based Petri net system. Then, put forward the parallel algorithm of Petri net based on multicore clusters according to the MPI+ OpenMP+ STM (STM, Software Transactional Memory and transactional memory) three-level parallel programming model and combining with the parallelized analysis of the changes of internal subnets and among the subnets. The experiment results show that the algorithm can better reflect the actual running process of Petri net system, and it is a feasible and effective method of realizing the parallel control and running of Petri net system.
Image restoration is a necessary preprocessing step for infrared remote sensing applications. Traditional methods allow us to remove the noise but penalize too much the gradients corresponding to edges. Image restorat...
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ISBN:
(纸本)9781628418569
Image restoration is a necessary preprocessing step for infrared remote sensing applications. Traditional methods allow us to remove the noise but penalize too much the gradients corresponding to edges. Image restoration techniques based on variational approaches can solve this over-smoothing problem for the merits of their well-defined mathematical modeling of the restore procedure. The total variation (TV) of infrared image is introduced as a L-1 regularization term added to the objective energy functional. It converts the restoration process to an optimization problem of functional involving a fidelity term to the image data plus a regularization term. Infrared image restoration technology with TV-L-1 model exploits the remote sensing data obtained sufficiently and preserves information at edges caused by clouds. Numerical implementation algorithm is presented in detail. Analysis indicates that the structure of this algorithm can be easily implemented in parallelization. Therefore a parallel implementation of the TV-L-1 filter based on multicore architecture with shared memory is proposed for infrared real-time remote sensing systems. Massive computation of image data is performed in parallel by cooperating threads running simultaneously on multiple cores. Several groups of synthetic infrared image data are used to validate the feasibility and effectiveness of the proposed parallel algorithm. Quantitative analysis of measuring the restored image quality compared to input image is presented. Experiment results show that the TV-L-1 filter can restore the varying background image reasonably, and that its performance can achieve the requirement of real-time image processing.
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