An exploration of local rules to map spawning processes to regular hardware architectures

Abstract

This thesis presents an exploration of population growth via simulation in software to ascertain
if a massively parallel hardware system can manage applications running within.
Task execution happens dynamically and is controlled by the growth mechanism implementing
efficient mapping in simulation.
Algorithms that provide population simulation models are often inspired by those
evidenced in biology and in particular those of cellular automata and L-systems. These
algorithms are of particular interest due to their complexity and self-replication and
recent research has shown that it is the refinement of the biological methodology that
has resulted in their complexity. Further to this, adaptation of the design has moved the
algorithm on towards being able to organize and build itself from a single cell. A growth
model is utilized in software systems to provide production of meaningful data. The
development of bio-inspired software is constrained by using contemporary processor
architectures.

Metadata

Supervisors:	Stepney, Susan and Tempesti, Gianluca
Keywords:	growth; simulation; mapping
Awarding institution:	University of York
Academic Units:	The University of York > Computer Science (York)
Depositing User:	Mr Stewart Aitken
Date Deposited:	04 Aug 2022 10:43
Last Modified:	04 Aug 2022 10:43
Open Archives Initiative ID (OAI ID):	oai:etheses.whiterose.ac.uk:31174

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An exploration of local rules to map spawning processes to regular hardware architectures

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