A parallel approach to the picture restoration algorithm of Geman and Geman on an SIMD machine
Image and Vision Computing
A Mapping Strategy for Parallel Processing
IEEE Transactions on Computers
A Partitioning Strategy for Nonuniform Problems on Multiprocessors
IEEE Transactions on Computers
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Transputer reference manual
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
The design and analysis of parallel algorithms
The design and analysis of parallel algorithms
Entropy driven artifical neuronal networks and sensorial representation: a proposal
Journal of Parallel and Distributed Computing - Neural Computing
Mapping neural networks onto message-passing multicomputers
Journal of Parallel and Distributed Computing - Neural Computing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Transparent problem decomposition and mapping-A CSTools based implementation
Proceedings of the world transputer user group (WOTUG) conference on Transputing '91
IEEE Transactions on Computers
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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While simulation programs for single neural networks, running on parallel machines, always use a fixed problem decomposition and mapping strategy, we show that this is not possible for modular neural networks. We demonstrate this by analysing decomposition and mapping issues for a particular modular neural network model: the entropy-driven artificial neural network. The classic approach to simulations consists of two steps: first a data structure is built, describing the problem to be simulated. For a neural network this data structure contains the network topology, the interconnection strengths, etc. In a second step, this data structure is read into the simulation program, which performs a fixed decomposition and mapping before simulation can take place. Since this approach cannot be used any more for simulations of modular networks, we propose a new, three-step approach, in which decomposition and mapping are taken out of the simulation program. A compiler is used to prepare the problem data structure, a splitter program takes care of problem decomposition, and the simulator program takes the decomposed problem as its input. Since all decisions with respect to decomposition and mapping are taken by the splitter, the simulator program is independent of decomposition and mapping, and hence it can handle any decomposition and mapping. Following this approach, a machine-independent simulation environment was designed, and this design was implemented on a transputer system. To show that our approach is generic (i.e. not limited to simulations of modular networks) an implementation of a Hopfield network for image restoration is described. In spite of the classic preconceptions about generic software, performance analysis and benchmark results show that our novel, generic approach can be implemented efficiently on transputer arrays.