9 research outputs found

    Parallel backpropagation neural networks forTask allocation by means of PVM

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    Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network is, by itself, an inherently parallel system where many, extremely simple, processing units work simultaneously in the same problem building up a computational device which possess adaptation (learning) and generalisation (recognition) abilities. Implementation of neural networks roughly involve at least three stages; design, training and testing. The second, being CPU intensive, is the one requiring most of the processing resources and depending on size and structure complexity the learning process can be extremely long. Thus, great effort has been done to develop parallel implementations intended for a reduction of learning time. Pattern partitioning is an approach to parallelise neural networks where the whole net is replicated in different processors and the weight changes owing to diverse training patterns are parallelised. This approach is the most suitable for a distributed architecture such as the one considered here. Incoming task allocation, as a previous step, is a fundamental service aiming for improving distributed system performance facilitating further dynamic load balancing. A Neural Network Device inserted into the kernel of a distributed system as an intelligent tool, allows to achieve automatic allocation of execution requests under some predefined performance criteria based on resource availability and incoming process requirements. This paper being, a twofold proposal, shows firstly, some design and implementation insights to build a system where decision support for load distribution is based on a neural network device and secondly a distributed implementation to provide parallel learning of neural networks using a pattern partitioning approach. In the latter case, some performance results of the parallelised approach for learning of backpropagation neural networks, are shown. This include a comparison of recall and generalisation abilities and speed-up when using a socket interface or PVM.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

    Parallel backpropagation neural networks forTask allocation by means of PVM

    Get PDF
    Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network is, by itself, an inherently parallel system where many, extremely simple, processing units work simultaneously in the same problem building up a computational device which possess adaptation (learning) and generalisation (recognition) abilities. Implementation of neural networks roughly involve at least three stages; design, training and testing. The second, being CPU intensive, is the one requiring most of the processing resources and depending on size and structure complexity the learning process can be extremely long. Thus, great effort has been done to develop parallel implementations intended for a reduction of learning time. Pattern partitioning is an approach to parallelise neural networks where the whole net is replicated in different processors and the weight changes owing to diverse training patterns are parallelised. This approach is the most suitable for a distributed architecture such as the one considered here. Incoming task allocation, as a previous step, is a fundamental service aiming for improving distributed system performance facilitating further dynamic load balancing. A Neural Network Device inserted into the kernel of a distributed system as an intelligent tool, allows to achieve automatic allocation of execution requests under some predefined performance criteria based on resource availability and incoming process requirements. This paper being, a twofold proposal, shows firstly, some design and implementation insights to build a system where decision support for load distribution is based on a neural network device and secondly a distributed implementation to provide parallel learning of neural networks using a pattern partitioning approach. In the latter case, some performance results of the parallelised approach for learning of backpropagation neural networks, are shown. This include a comparison of recall and generalisation abilities and speed-up when using a socket interface or PVM.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

    A parallel approach for backpropagation learning of neural networks

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    Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly describes parallel schemes for a backpropagation algorithm and proposes a distributed system architecture for developing parallel training with a partition pattern scheme. Under this approach, weight changes are computed concurrently, exchanged between system components and adjusted accordingly until the whole parallel learning process is completed. Some comparative results are also shown.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI

    A genetic approach using direct representation of solution for the parallel task scheduling problem

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    In scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program can be conceived as a set of parallel components (tasks) which can be executed according to some precedence relationship. In this case efficient scheduling of tasks permits to take full advantage of the computational power provided by a multiprocessor or a multicomputer system. This kind of planning involves the assignment of partially ordered tasks onto the system architecture processing components. This paper shows the problem of allocating a number of non-identical tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. The well-known Graham’s list scheduling algorithm (LSA) is contrasted with an evolutionary approach using a direct representation of solutions.Eje: Computación evolutivaRed de Universidades con Carreras en Informática (RedUNCI

    Evolutionary approaches for the parallel task scheduling problem : the representation issue

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    The problem of how to find a schedule on m > 2 processors of equal capacity that minimises the whole processing time of independent tasks has been shown as belonging to the NP-complete class (Horowitz and Sahni [12]). Evolutionary Algorithms (EAs) have been used in the past to implement the allocation of the components (tasks) of a parallel program to processors [12], [13], [14], [16], [17]. Those approaches showed their advantages when contrasted against conventional approaches and different chromosome representations were proposed. This paper shows four algorithms to solve the problem of allocating a number of non-identical related tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. Three evolutionary algorithms, using an indirect-decode representation, are contrasted with the well-known Graham’s [11] list scheduling algorithm (LSA). All of them use the conventional Single Crossover Per Couple (SCPC) approach and indirectdecode representation but they differ in what is represented by the decoders. In the first representation scheme, decoders represent processor dispatching priorities, in the second decoders represent tasks priority lists, and in the third decoders represent both processor dispatching priorities and tasks priority lists in a bipartite chromosome. Chromosome structure, genetic operators, experiments and results are discussed.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

    A parallel approach for backpropagation learning of neural networks

    Get PDF
    Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly describes parallel schemes for a backpropagation algorithm and proposes a distributed system architecture for developing parallel training with a partition pattern scheme. Under this approach, weight changes are computed concurrently, exchanged between system components and adjusted accordingly until the whole parallel learning process is completed. Some comparative results are also shown.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI

    A genetic approach using direct representation of solution for the parallel task scheduling problem

    Get PDF
    In scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program can be conceived as a set of parallel components (tasks) which can be executed according to some precedence relationship. In this case efficient scheduling of tasks permits to take full advantage of the computational power provided by a multiprocessor or a multicomputer system. This kind of planning involves the assignment of partially ordered tasks onto the system architecture processing components. This paper shows the problem of allocating a number of non-identical tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. The well-known Graham’s list scheduling algorithm (LSA) is contrasted with an evolutionary approach using a direct representation of solutions.Eje: Computación evolutivaRed de Universidades con Carreras en Informática (RedUNCI

    Evolutionary approaches for the parallel task scheduling problem : the representation issue

    Get PDF
    The problem of how to find a schedule on m > 2 processors of equal capacity that minimises the whole processing time of independent tasks has been shown as belonging to the NP-complete class (Horowitz and Sahni [12]). Evolutionary Algorithms (EAs) have been used in the past to implement the allocation of the components (tasks) of a parallel program to processors [12], [13], [14], [16], [17]. Those approaches showed their advantages when contrasted against conventional approaches and different chromosome representations were proposed. This paper shows four algorithms to solve the problem of allocating a number of non-identical related tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. Three evolutionary algorithms, using an indirect-decode representation, are contrasted with the well-known Graham’s [11] list scheduling algorithm (LSA). All of them use the conventional Single Crossover Per Couple (SCPC) approach and indirectdecode representation but they differ in what is represented by the decoders. In the first representation scheme, decoders represent processor dispatching priorities, in the second decoders represent tasks priority lists, and in the third decoders represent both processor dispatching priorities and tasks priority lists in a bipartite chromosome. Chromosome structure, genetic operators, experiments and results are discussed.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

    Unsupervised Algorithms for Learning Emergent Spatio-Temporal Correlations

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    Many applications require the extraction of spatiotemporal correlations among dynamically emergent features of non-stationary distributions. In such applications it is not possible to obtain an a priori analytical characterization of the emergent distribution. This paper extends the Growing Cell Structures (GCS) network and presents two novel (GIST and GEST) networks, which combine unsupervised feature-extraction and Hebbian learning, for tracking such emergent correlations. The networks were successfully tested on the challenging Data Mapping problem, using an execution driven simulation of their implementation in hardware. The results of the simulations show the successful use of the GIST and GEST networks for extracting spatiotemporal correlation information among emergent features of previously unknown distributions and, indicate the feasibility of hardware implementation for online use. Of the two networks, the GEST network evinced better performance in terms of the network map stability, feature/correlation tracking ability and network sizes evolved
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