Activity in PythonPDEVS

Tendeloo, Yentl Van; Vangheluwe, Hans

doi:10.1051/itmconf/20140301002

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…But whereas PythonPDEVS only supports optimistic synchronization, dxex support multiple synchronization protocols (though not distributed). This is in line with the design principle of PythonPDEVS : allow users to pass performance hints 16 to the simulation kernel. In our case, a user can configure the simulation kernel with the synchronization protocol to use, or even switch the synchronization protocol during runtime.…”

Section: Devs-ex-machinasupporting

confidence: 62%

“…Contrary to PythonPDEVS, however, dxex does not support distributed simulation, 15 model migrations 31 or activity hints. 16 Although PythonPDEVS offers very fast turnaround cycles, simulation performance was easily outdone by compiled simulation kernels. In terms of performance, adevs 10 offered much faster simulation, at the cost of compilation time.…”

Section: Related Workmentioning

confidence: 99%

“…Contrary to PythonPDEVS , however, dxex does not support distributed simulation, 15 model migrations 31 or activity hints. 16…”

Section: Related Workmentioning

confidence: 99%

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A PDEVS simulator supporting multiple synchronization protocols: implementation and performance analysis

et al. 2017

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With the ever-increasing complexity of simulation models, parallel simulation becomes necessary to perform simulation within reasonable time bounds. The built-in parallelism of Parallel DEVS is often insufficient to tackle this problem on its own. Several synchronization protocols have been proposed, each with their distinct advantages and disadvantages. Due to the significantly different implementation of these protocols, most Parallel DEVS simulation tools are limited to only one such protocol. In this paper, we present a Parallel DEVS simulator, grafted on C+ +11 and based on PythonPDEVS, supporting both conservative and optimistic synchronization protocols. The simulator not only supports both protocols but also has the capability to switch between them at runtime. The simulator can combine each synchronization protocols with either a threaded or sequential implementation of the PDEVS protocol. We evaluate the performance gain obtained by choosing the most appropriate synchronization protocol. A comparison is made to adevs in terms of CPU time and memory usage, to show that our modularity does not hinder performance. We compare the speedup obtained by synchronization with that of the inherent parallelism of PDEVS in isolation and combination, and contrast the results with the theoretical limits. We further allow for an external component to gather simulation statistics, on which runtime switching between the different synchronization protocols can be based. The effects of allocation on our synchronization protocols are also studied.

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Section: Devs-ex-machinasupporting

confidence: 62%

Section: Related Workmentioning

confidence: 99%

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A PDEVS simulator supporting multiple synchronization protocols: implementation and performance analysis

et al. 2017

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“…Van Tendeloo and Vangheluwe demonstrate in their work that significant performance improvements can be obtained by injecting domain knowledge in their Python based distributed DEVS simulator PythonPDEVS [31]. Their work builds further on the abstract notion of activity introduced by Muzy et.…”

Section: Static Model Partitioningmentioning

confidence: 98%

Towards evaluating emergent behavior of the internet of things using large scale simulation techniques (wip)

Bosmans

Mercelis

Hellinckx

et al. 2018

Proceedings of the 4th ACM International Conference of Computing for Engineering and Sciences

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With the increase in Internet of Things devices and more decentralized architectures we see a new type of application gain importance, a type where local interactions between individual entities lead to a global emergent behavior, Emergent-based IoT (EBI) Systems. In this position paper we explore techniques to evaluate this emergent behavior in IoT applications. Because of the required scale and diversity this is not an easy task. Therefore, we mainly focus on a distributed simulation approach and provide an overview of possible techniques that could optimize the overall simulation performance. Our focus is both on modeling and simulation technology.

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“…This can be explained by the activity-based data structures implemented in PythonPDEVS. 54 As a list-based scheduler would be better in cases where many collisions occur, 22 the data structure modifies itself to a list-based one. VLE and ADEVS have a static, heap-based, scheduler, which is not optimized for this situation.…”

Section: Performancementioning

confidence: 99%

An evaluation of DEVS simulation tools

Tendeloo

Vangheluwe

2016

SIMULATION

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DEVS is a popular formalism for modeling complex dynamic systems using a discrete-event abstraction. Owing to its popularity, and the simplicity of the simulation kernel, a number of tools have been constructed by academia and industry. However, each of these tools has distinct design goals and a specific programming language implementation. Consequently, each supports a specific set of formalisms, combined with a specific set of features. Performance differs significantly between different tools. We provide an overview of the current state of eight different DEVS simulation tools: ADEVS, CD++, DEVS-Suite, MS4 Me, PowerDEVS, PythonPDEVS, VLE, and X-S-Y. We compare supported formalisms, compliance, features, and performance. This paper aims to help modelers in deciding which tools to use to solve their specific problems. It further aims to help tool builders, by showing the aspects of their tools that could be extended in future tool versions.

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Activity in PythonPDEVS

Cited by 11 publications

References 6 publications

A PDEVS simulator supporting multiple synchronization protocols: implementation and performance analysis

A PDEVS simulator supporting multiple synchronization protocols: implementation and performance analysis

Towards evaluating emergent behavior of the internet of things using large scale simulation techniques (wip)

An evaluation of DEVS simulation tools

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