## Massively Parallel Models of Computation: Distributed Parallel Processing in Artificial Intelligence and Optimisation |

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### Contents

Background | 9 |

Fundamentals of distributed parallel computation | 19 |

Timing and synchronization | 49 |

Copyright | |

11 other sections not shown

### Common terms and phrases

acyclic orientation additional Algorithm Gsr allocation function analog Hopfield neural ARTIFICIAL INTELLIGENCE asynchronous model Barbosa Bayesian network becomes a sink behavior BIBLIOGRAPHIC NOTES binary Hopfield neural Boltzmann machines buffer cellular automata channel Chapter combinatorial optimization communication comp.msg complexity conditional probabilities context corresponding denote detection directed cycle directed graph discussed in Section distributed algorithm distributed computation distributed parallel algorithm distributed parallel simulation edge reversal equations Euler's method Figure Gibbs sampling given in Section global termination heuristic Hopfield neural network initial input linear system Markov random fields minima MNCP MSGi neighbors neurons node becomes node cover notation Occam program P(di pair parallel algorithm parallel computation parallel processing PC automaton network procedures processor pi properties received routing Runge-Kutta method scheduling by edge Send sent simulated annealing solution stochastic simulation subset synaptic strength synchronous task allocation undirected updating function variables verb