This letter introduces a parallel and distributed computation method for dynamical economic dispatch over a cyber-physical system. To achieve a faster economic dispatch operation, accelerated consensus approach is pro...
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This letter introduces a parallel and distributed computation method for dynamical economic dispatch over a cyber-physical system. To achieve a faster economic dispatch operation, accelerated consensus approach is proposed. The simulation illustrates the better performance of accelerated consensus algorithm.
As one of the most fundamental networks for parallel and distributed computation, cycle is suitable for developing simple algorithms with low communication cost. A graph G is called k-fault-tolerant edge-pancyclic if ...
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As one of the most fundamental networks for parallel and distributed computation, cycle is suitable for developing simple algorithms with low communication cost. A graph G is called k-fault-tolerant edge-pancyclic if after deleting any faulty set F of k vertices and/or edges from G, every correct edge in the resulting graph lies in a cycle of every length from g to vertical bar V (G - F)vertical bar, inclusively, where g is the girth of G, the length of a shortest cycle in G. The n-dimensional crossed cube CQ(n) is an important variant of the hypercube Q(n), which possesses some properties superior to the hypercube. This paper investigates the fault-tolerant edge-pancyclicity of CQ(n), and shows that if CQ(n)(n >= 5) contains at most n - 2 faulty vertices and/or edges then, for any fault-free edge uv and every length l from 6 to vertical bar V (CQ(n) - F)vertical bar except l = 7, there is a fault-free cycle of length l containing the edge uv. The result is optimal in some senses.
This paper proposes a preliminary microfluidic computing system design for Spiking Neural P systems designed to solve the computational hard problem of Boolean satisfiability SAT by implementing the model studied in o...
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This paper proposes a preliminary microfluidic computing system design for Spiking Neural P systems designed to solve the computational hard problem of Boolean satisfiability SAT by implementing the model studied in our previous work. We have also developed a simulation model for the proposed system and have been doing in silico experiments. An AC voltage applied to facilitated electrodes generates Dielectrophoretic force (DEP) and non-uniform electric field in the microfluidic channels. This DEP serves as the main functioning tool of the proposed biochip to control computation steps.
This paper, the second one in a three-paper sequence, presents the nesC model of a Hopfield neural network configured for a static optimization problem, the maximum independent set, in fully parallel and distributed m...
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This paper, the second one in a three-paper sequence, presents the nesC model of a Hopfield neural network configured for a static optimization problem, the maximum independent set, in fully parallel and distributed mode for TinyOS-based wireless sensor networks. Actual nesC code that implements the required neural computing functionality is presented. The graph representation of the maximum independent set problem is used as the basis for the topology of the Hopfield network as well as the wireless sensor network since each mote is conceived to house one neuron in order to facilitate fully parallel and distributed computation. The nesC implementation of a multitude of phases of computation is detailed including initialization of the neural network, relaxation, convergence detection, and solution detection all while the neural computations are performed on the wireless sensor network. Simulation of the presented nesC-TinyOS model is deferred to the third paper in the sequence. (C) 2011 Published by Elsevier B.V.
computations based on graphs are very common problems but complexity, increasing size of analyzed graphs and a huge amount of communication make this analysis a challenging task. In this paper, we present a comparison...
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ISBN:
(纸本)9783319780542;9783319780535
computations based on graphs are very common problems but complexity, increasing size of analyzed graphs and a huge amount of communication make this analysis a challenging task. In this paper, we present a comparison of two parallel BFS (Breath- First Search) implementations: MapReduce run on Hadoop infrastructure and in PGAS (Partitioned Global Address Space) model. The latter implementation has been developed with the help of the PCJ (parallelcomputations in Java) - a library for parallel and distributed computations in Java. Both implementations realize the level synchronous strategy - Hadoop algorithm assumes iterative MapReduce jobs, whereas PCJ uses explicit synchronization after each level. The scalability of both solutions is similar. However, the PCJ implementation is much faster (about 100 times) than the MapReduce Hadoop solution.
This paper, the second one in a three-paper sequence, presents the nesC model of a Hopfield neural network configured for a static optimization problem, the maximum independent set, in fully parallel and distributed m...
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This paper, the second one in a three-paper sequence, presents the nesC model of a Hopfield neural network configured for a static optimization problem, the maximum independent set, in fully parallel and distributed mode for TinyOS-based wireless sensor networks. Actual nesC code that implements the required neural computing functionality is presented. The graph representation of the maximum independent set problem is used as the basis for the topology of the Hopfield network as well as the wireless sensor network since each mote is conceived to house one neuron in order to facilitate fully parallel and distributed computation. The nesC implementation of a multitude of phases of computation is detailed including initialization of the neural network, relaxation, convergence detection, and solution detection all while the neural computations are performed on the wireless sensor network. Simulation of the presented nesC-TinyOS model is deferred to the third paper in the sequence.
We propose a new and low per-iteration complexity first-order primal-dual optimization framework for a convex optimization template with broad applications. Our analysis relies on a novel combination of three classic ...
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We propose a new and low per-iteration complexity first-order primal-dual optimization framework for a convex optimization template with broad applications. Our analysis relies on a novel combination of three classic ideas applied to the primal-dual gap function: smoothing, acceleration, and homotopy. The algorithms due to the new approach achieve the best-known convergence rate results, in particular when the template consists of only nonsmooth functions. We also outline a restart strategy for the acceleration to significantly enhance the practical performance. We demonstrate relations with the augmented Lagrangian method and show how to exploit the strongly convex objectives with rigorous convergence rate guarantees. We provide representative examples to illustrate that the new methods can outperform the state of the art, including Chambolle Pock, and the alternating direction method-of-multipliers algorithms. We also compare our algorithms with the well-known Nesterov smoothing method.
Graph-based computations are used in many applications. Increasing size of analyzed data and its complexity make graph analysis a challenging task. In this paper we present performance evaluation of Java implementatio...
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ISBN:
(纸本)9783319321523;9783319321516
Graph-based computations are used in many applications. Increasing size of analyzed data and its complexity make graph analysis a challenging task. In this paper we present performance evaluation of Java implementation of Graph500 benchmark. It has been developed with the help of the PCJ (parallelcomputations in Java) library for parallel and distributed computations in Java. PCJ is based on a PGAS (Partitioned Global Address Space) programming paradigm, where all communication details such as threads or network programming are hidden. In this paper, we present Java implementation details of first and second kernel from Graph500 benchmark. The results are compared with the existing MPI implementations of Graph500 benchmark, showing good scalability of PCJ library.
This paper introduces a new PARAFAC algorithm for a class of third-order tensors. Particularly, the proposed algorithm is based on subspace estimation and solving a non-symmetrical joint diagonalization problem. To de...
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A parallel algorithm for low-rank tensor decomposition that is especially well-suited for big tensors is proposed. The new algorithm is based on parallel processing of a set of randomly compressed, reduced-size 'r...
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ISBN:
(纸本)9781479928934
A parallel algorithm for low-rank tensor decomposition that is especially well-suited for big tensors is proposed. The new algorithm is based on parallel processing of a set of randomly compressed, reduced-size 'replicas' of the big tensor. Each replica is independently decomposed, and the results are joined via a master linear equation per tensor mode. The approach enables massive parallelism with guaranteed identifiability properties: if the big tensor has low rank and the system parameters are appropriately chosen, then the rank-one factors of the big tensor will be exactly recovered from the analysis of the reduced-size replicas. The proposed algorithm is proven to yield memory / storage and complexity gains of order up to IJ/F for a big tensor of size I x J x K of rank F with F <= I <= J <= K.
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