We present a high-bandwidth multichannel time slot permuter handling N! permutations on N=2/sup n/ optical time division multiplexed (TDM) inputs arranged as /spl radic/N parallel frames with /spl radic/N time slots e...
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We present a high-bandwidth multichannel time slot permuter handling N! permutations on N=2/sup n/ optical time division multiplexed (TDM) inputs arranged as /spl radic/N parallel frames with /spl radic/N time slots each. A generalized non-blocking property of the reverse banyan network is obtained and is used to derive a self-routing space-domain network and its counterpart in the time domain. The network is fully pipelineable and self-routing with distributed control obtained dynamically at each switch. The permuter uses O(/spl radic/N(n/sup 2/)) single-bit controlled 2/spl times/2 optical switching elements with feedforward and feedback fiber delay lines, (which is a factor of O(n) reduction in hardware compared to previously proposed multichannel time slot permuters), and provides higher throughputs as compared to earlier Benes based multichannel permuters. The network preserves frame integrity and has on initial frame delay which is linear in the number of time slots.
The computation of a one-dimensional FFT on a c-dimensional torus multicomputer is analyzed. Different approaches are proposed which differ in the way they use the interconnection network. The first approach is based ...
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The computation of a one-dimensional FFT on a c-dimensional torus multicomputer is analyzed. Different approaches are proposed which differ in the way they use the interconnection network. The first approach is based on the multidimensional index mapping technique for the FFT computation. The second approach starts from a hypercube algorithm and then embeds the hypercube onto the torus. The third approach reduces the communication cost of the hypercube algorithm by pipelining the communication operations. A novel methodology to pipeline the communication operations on a torus is proposed. Analytical models are presented to compare the different approaches. This comparison study shows that the best approach depends on the number of dimensions of the torus and the communication start-up and transfer times. The analytical models allow us to select the most efficient approach for the available machine.< >
A performance-price ratio of 2.2 Gflops per million dollars is demonstrated in simulations of wind instruments using a cluster of 20 non-dedicated Hewlett-Packard workstations and a shared-bus Ethernet network (the pe...
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A performance-price ratio of 2.2 Gflops per million dollars is demonstrated in simulations of wind instruments using a cluster of 20 non-dedicated Hewlett-Packard workstations and a shared-bus Ethernet network (the performance-price ratio is calculated assuming that the workstations were dedicated). The present simulations of wind instruments are based on the compressible Navier Stokes equations, and have only recently become possible through the use of parallel computing and through developments in numerical methods. The distributed simulation system has been built directly on top of UNIX and TCP/IP, and includes automatic process migration from busy workstations to free workstations.
Because of their remarkable ability to learn nonlinear mappings from a nonexhaustive training set and generalize, neurocomputers can readily play the role of learning controllers for complex plants. Due to the paralle...
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Because of their remarkable ability to learn nonlinear mappings from a nonexhaustive training set and generalize, neurocomputers can readily play the role of learning controllers for complex plants. Due to the paralleldistributed nature of processing they introduce little time delay in the control loop. In this paper a new generalized feedforward neural network model is applied to the task of controlling an aircraft engine model. A more effective learning algorithm based on adaptive optimization is presented which compares favorably with the classical backpropagation method. Similar controllers can be designed for critical industrial applications such as nuclear power plants etc.< >
Training set parallelization is an efficient method to optimize the training procedure performance of the backpropagation neural network algorithm. In training set parallelism, the training patterns are distributed ...
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Training set parallelization is an efficient method to optimize the training procedure performance of the backpropagation neural network algorithm. In training set parallelism, the training patterns are distributed 'optimally' among a heterogeneous array of processors, optimality criterion obtain the minimum training time per epoch. Earlier studies on heterogeneous transputers connected in a pipeline-ring topology have indicated that the above optimization problem results in a mixed integer programming problem and results in large computation time to find the optimal pattern allocations. In this paper, a genetic algorithm is used as an optimization tool to find the optimal allocation of patterns. The approach is illustrated using two benchmark problems, the 256-8-256 Encoder and NETTALK problems. Results indicate that when 'a priori' information is not used, the computation time needed by the genetic algorithm is comparable to that obtained by mixed integer programming. However, when 'a priori' information is used, the genetic algorithm results in significant reduction in computation time for finding the optimal solution.
parallel computing architectures are a promising answer to the growing needs of computing power in many application fields. This model can be easily fitted to applications that are structured as a collection of weakly...
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parallel computing architectures are a promising answer to the growing needs of computing power in many application fields. This model can be easily fitted to applications that are structured as a collection of weakly coupled processes. Every process evolves independently and synchronises periodically with the others by exchanging relatively small messages. The power plant simulators developed at our Research Center are an example of this kind of application. Due to the absence of standards in this field, our activity was focused upon the implementation of a distributed software platform, open to heterogeneous systems, and to be used as a reference for our future developments. In this paper, after a brief description of the simulation environment, we describe a first parallel version based on a Transputer network and then the current evolution based on a network of heterogeneous workstations.
As part of a larger project concerned with the unification of programming paradigms, this paper describes the development of a model of computation for the relational paradigm, as represented by Prolog. The model is b...
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A parallel implementation of the numerical simulation of a GaAs MESFET device based on the energy transport model using the finite difference discretization scheme and solved by a point iterative method is presented. ...
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The paper describes a distributed system architecture based on nodes (N-cube modules) connected by a Local Area network. The system has been developed to support the implementation of new services in a B-ISDN testbed ...
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This paper presents the design and implementation of a paralleldistributed control architecture for industrial multiple robot systems. The design methodology is based on a concept of discrete states and actions, and ...
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This paper presents the design and implementation of a paralleldistributed control architecture for industrial multiple robot systems. The design methodology is based on a concept of discrete states and actions, and a robotic task is represented as a sequence of primitive actions. For cooperative or exclusive tasks at the synchronous level of multiple robot systems, Petri net representation is applied, and discrete event-driven control is implemented as a data flow network of concurrent processes communicating with each other. Implementation of multiprocessing control on a microcomputer and a network of microcomputers is discussed.
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