Birds on the wall: Distributing a process-oriented simulation

Sampson, Adam T.; Bjørndalen, John Markus; Andrews, Paul S.

doi:10.1109/cec.2009.4982952

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…This is one of the patterns developed for modelling complex systems as part of the (UK EPSRC funded) CoSMoS project [47,41,3,38,29,32]. It addresses the modelling of space and time for an environment in which mobile agents can roam and interact, and from which interesting behaviours emerge.…”

Section: Cosmos Space and Timementioning

confidence: 99%

To boldly go: an occam-π mission to engineer emergence

et al. 2012

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Future systems will be too complex to design and implement explicitly. Instead, we will have to learn to engineer complex behaviours indirectly: through the discovery and application of local rules of behaviour, applied to simple process components, from which desired behaviours predictably emerge through dynamic interactions between massive numbers of instances. This paper describes a process-oriented architecture for fine-grained concurrent systems that enables experiments with such indirect engineering. Examples are presented showing the differing complex behaviours that can arise from minor (non-linear) adjustments to low-level parameters, the difficulties in suppressing the emergence of unwanted (bad) behaviour, the unexpected relationships between apparently unrelated physical phenomena (shown up by their separate emergence from the same primordial process swamp) and the ability to explore and engineer completely new physics (such as force fields) by their emergence from low-level process interactions whose mechanisms can only be imagined, but not built, at the current time. Keywords complex systems • emergent behaviour • emergent relations • emergent discovery • process orientation • mobile processes • occam-pi

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Section: Cosmos Space and Timementioning

confidence: 99%

To boldly go: an occam-π mission to engineer emergence

et al. 2012

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“…The design model was close to the simulation model, with clear mappings from lymphocyte states to occam-π processes. The implementation re-used patterns from existing (well-engineered and verified) occam-π simulations (see [25], [31], [32]). …”

Section: ) Domain and Domain Modellingmentioning

confidence: 99%

Reflections on the Simulation of Complex Systems for Science

Polack

Andrews

Ghetiu

et al. 2010

2010 15th IEEE International Conference on Engineering of Complex Computer Systems

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In studying complex systems, agent-based simulations offer the possibility of directly modelling components in an environment. However, the scientific value of agent-based simulations has been limited by inadequate scientific rigour. The paper focuses on agent-based simulations that are used in biological and bio-medical research. Starting from a review of best practice in simulation engineering, the paper identifies some of the key activities in developing complex systems simulations that support scientific research, and how these contribute to the essential development of mutual trust among developers and scientists. Examples from the authors' own experience illustrate how a range of studies have manifested these key activities, and identifies some successes and problems encountered.

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“…occam-π's excellent support for safe, lightweight, message-passing concurrency and its highly-efficient multicore runtime system make it straightforward to build agent-based simulations in which agents are implemented directly as lightweight processes. Such process-oriented simulations automatically make good use of modern multicore processors, and can be distributed over clusters of machines [5,6]. However, occam-π's limited set of data types and lack of bindings for external libraries makes data analysis and visualisation more complex than in other languages.…”

Section: Prototype Implementationmentioning

confidence: 99%

The best of most worlds

Sampson

Andrews

2010

Proceedings of the 9th Workshop on Parallel/High-Performance Object-Oriented Scientific Computing

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Computing techniques are increasingly being used in scientific research to tackle a diverse set of problems. An example is complex systems research, which focuses on the use of computer simulations to explore, understand and describe the real-world system under study. These simulations are often sophisticated pieces of software with numerous design trade-offs between performance and ease of development and use. We propose a simulation framework for complex systems simulation that allows each component of a simulation-for example visualisation, or data analysis-to be developed in the most appropriate language. The framework uses the concept of shared objects to communicate data between simulation components. We present here a detailed motivation for multilingual simulations, an outline design and prototype for the simulation framework, and discuss future plans for the framework.

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Birds on the wall: Distributing a process-oriented simulation

Cited by 7 publications

References 12 publications

To boldly go: an occam-π mission to engineer emergence

To boldly go: an occam-π mission to engineer emergence

Reflections on the Simulation of Complex Systems for Science

The best of most worlds

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