Supporting Load Distribution Strategies in Message-Passing Multiprocessors: a case study

This paper describes a software architecture designed as a support for tackling the load distribution problem when solving complex problems on concurrent processors. We have considered transputer-based MIMD multiprocessors as concurrent processors and a simulator for biologically inspired neural networks as a case study. Biologically inspired neural networks are characterized by having many thousands of neurons and synapses and topologically based connection schemes. It has been our main aim to give the user the possibility of simply defining and modifying widely differing load distribution strategies, in order to make it possible to deal with a broad range of neural network architectures and processor topologies. Furthermore we provide a real tool for hiding communication delays.