A comparative simulation study of the performance of single-bus and two-bus multiprocessors

Article Abstract:

This paper represents a comparative simulation modeling of the performance of the uniprocessor, single-bus multiprocessor, and two-bus multiprocessor computer systems. The performance indexes of the single-bus and two-bus multiprocessor systems compared to the uniprocessor system, that are used in the modeling, are processor speedup factors S(subscript P1) and S(subscript P2) respectively. A third performance index, Bus-speedup factor, S(subscript B), is derived to compare the performance of the two-bus and single-bus systems. The first two indexes provide measures of the processing speedup improvement of both multiprocessors with respect to the uniprocessor system, while the third index provides a measure of the performance improvement resulting from adding an extra bus to the single-bus multiprocessor system. Three data transfer protocols are considered in this work: First Come First Served (FCFS), Token Ring (TR), and the Priority (PR) policy. Simulation experiments show that increasing the number of processors in the considered multiprocessor architectures does not necessarily improve the overall performance. Moreover, adding an extra bus to the single-bus architecture provides some speedup improvement that depends on the nature of the task program and the data transfer protocol. Simulation results show that FCFS and TR scheduling policies provide better performance than the PR policy. However, FCFS requires relatively less hardware and software complexity than the TR. (Reprinted by permission of the publisher.)

Buses (Transportation), Benchmarking, Modeling, Data modeling software, Multiprocessing, Benchmark, Comparison, Performance Measurement, Study, Simulation Theory, Buses, Simulation of Computer Systems

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Parallel simulation of neural networks

Article Abstract:

We simulate five neural networks on a vector multiprocessor. The training time can be reduced significantly especially when the training data size is large. These five neural networks are: (1) the ART 1 network, (2) the ART 2 network, (3) the feedforward network, (4) the recurrent network, and (5) the Hopfield network. The training algorithms are programmed in such a way to best utilize (1) the inherent parallelism in neural computing, and (2) the vector and concurrent operations available on the parallel machine. To prove the correctness of parallelized training algorithms, each neural network is trained to perform a specific function. ART 1 and ART 2 are trained to recognize binary and analog patterns. The feedforward network is trained to perform the Fourier transform, the recurrent network is trained to predict the solution of a delay differential equation, the Hopfield network is trained to solve the traveling salesman problem. The machine we experiment with is the Alliant FX/80. (Reprinted by permission of the publisher.)

Neural networks, Algorithms, Artificial intelligence, Problem solving, Algorithm, Parallel processing, Simulation, Computer Learning, Models of Computation, Neural Network

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