79 research outputs found
Road Network Simulation Using FLAME GPU
Demand for high performance road network simulation is increasing due to the need for improved traffic management to cope with the globally increasing number of road vehicles and the poor capacity utilisation of existing infrastructure. This paper demonstrates FLAME GPU as a suitable Agent Based Simulation environment for road network simulations, capable of coping with the increasing demands on road network simulation. Gippsâ car following model is implemented and used to demonstrate the performance of simulation as the problem size is scaled. The performance of message communication techniques has been evaluated to give insight into the impact of runtime generated data structures to improve agent communication performance. A custom visualisation is demonstrated for FLAME GPU simulations and the techniques used are described
A formula-driven scalable benchmark model for ABM, applied to FLAME GPU
Agent Based Modelling (ABM) systems have become a popular technique for describing complex and dynamic systems. ABM is the simulation of intelligent agents and how these agents communicate with each other within the model. The growing number of agent-based applications in the simulation and AI fields led to an increase in the number of studies that focused on evaluating modelling capabilities of these applications. Observing system performance and how applications behave during increases in population size is the main factor for benchmarking in most of these studies. System scalability is not the only issue that may affect the overall performance, but there are some issues that need to be dealt with to create a standard benchmark model that meets all ABM criteria. This paper presents a new benchmark model and benchmarks the performance characteristics of the FLAME GPU simulator as an example of a parallel framework for ABM. The aim of this model is to provide parameters to easily measure the following elements: system scalability, system homogeneity, and the ability to handle increases in the level of agent communications and model complexity. Results show that FLAME GPU demonstrates near linear scalability when increasing population size and when reducing homogeneity. The benchmark also shows a negative correlation between increasing the communication complexity between agents and execution time. The results create a baseline for improving the performance of FLAME GPU and allow the simulator to be contrasted with other multi-agent simulators
Performance Optimization and Statistical Analysis of Basic Immune Simulator (BIS) Using the FLAME GPU Environment
Agent-based models (ABMs) are increasingly being used to study population dynamics in complex systems such as the human immune system. Previously, Folcik et al. developed a Basic Immune Simulator (BIS) and implemented it using the RePast ABM simulation framework. However, frameworks such as RePast are designed to execute serially on CPUs and therefore cannot efficiently handle large simulations. In this thesis, we developed a parallel implementation of immune simulator using FLAME GPU, a parallel ABM simulation framework designed to execute of Graphics Processing Units(GPUs). The parallel implementation was tested against the original RePast implementation for accuracy by running a simulation of immune response to a viral infection of generic tissue cells. Finally, a performance benchmark done against the original RePast implementation demonstrated a significant performance gain 13X for the parallel FLAME GPU implementation
Data-parallel agent-based microscopic road network simulation using graphics processing units
Road network microsimulation is computationally expensive, and existing state of the art commercial tools use task parallelism and coarse-grained data-parallelism for multi-core processors to achieve improved levels of performance. An alternative is to use Graphics Processing Units (GPUs) and fine-grained data parallelism. This paper describes a GPU accelerated agent based microsimulation model of a road network transport system. The performance for a procedurally generated grid network is evaluated against that of an equivalent multi-core CPU simulation. In order to utilise GPU architectures effectively the paper describes an approach for graph traversal of neighbouring information which is vital to providing high levels of computational performance. The graph traversal approach has been integrated within a GPU agent based simulation framework as a generalised message traversal technique for graph-based communication. Speed-ups of up to 43âŻĂ⯠are demonstrated with increased performance scaling behaviour. Simulation of over half a million vehicles and nearly two million detectors at a rate of 25âŻĂ⯠faster than real-time is obtained on a single GPU
Simulating heterogeneous behaviours in complex systems on GPUs
Agent Based Modelling (ABM) is an approach for modelling dynamic systems and studying complex and emergent behaviour. ABMs have been widely applied in diverse disciplines including biology, economics, and social sciences. The scalability of ABM simulations is typically limited due to the computationally expensive nature of simulating a large number of individuals. As such, large scale ABM simulations are excellent candidates to apply parallel computing approaches such as Graphics Processing Units (GPUs). In this paper, we present an extension to the FLAME GPU 1 [1] framework which addresses the divergence problem, i.e. the challenge of executing the behaviour of non-homogeneous individuals on vectorised GPU processors. We do this by describing a modelling methodology which exposes inherent parallelism within the model which is exploited by novel additions to the software permitting higher levels of concurrent simulation execution. Moreover, we demonstrate how this extension can be applied to realistic cellular level tissue model by benchmarking the model to demonstrate a measured speedup of over 4x
Implementazione e analisi del modello Flocking con FLAME GPU
In questa tesi verranno studiati i modelli basati su agenti e i software al supporto delle simulazioni ad agenti. In particolare sarĂ approfondito il modello Flocking, che riguarda la formazione di stormi di uccelli in volo. Questo modello Ăš parte della libreria di un software per simulazioni ad agenti chiamato NetLogo, ma verrĂ implementato con un nuovo ambiente di sviluppo ad alte prestazioni che fa uso della GPU, chiamato FLAME GPU. Questa nuova implementazione permette al modello di essere simulato con una popolazione molto piĂč vasta. E' interessante studiare come si comporta il modello secondo alcuni parametri e come cambiano le prestazioni dalla versione in NetLogo.
Saranno analizzati e confrontati NetLogo e FLAME GPU, i due principali supporti per realizzare simulazioni ad agenti. In particolare sarĂ approfondito FLAME GPU, usato nellâimplementazione di Flocking. Inoltre sarĂ discussa anche lâimplementazione di un altro modello sulla formazione di stormi a âVâ chiamato Flocking Vee Formations
PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment
Agent-based models (ABMs) are increasingly being used to study population dynamics in complex systems, such as the human immune system. Previously, Folcik et al. (The basic immune simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model 2007; 4: 39) developed a Basic Immune Simulator (BIS) and implemented it using the Recursive Porous Agent Simulation Toolkit (RePast) ABM simulation framework. However, frameworks such as RePast are designed to execute serially on central processing units and therefore cannot efficiently handle large model sizes. In this paper, we report on our implementation of the BIS using FLAME GPU, a parallel computing ABM simulator designed to execute on graphics processing units. To benchmark our implementation, we simulate the response of the immune system to a viral infection of generic tissue cells. We compared our results with those obtained from the original RePast implementation for statistical accuracy. We observe that our implementation has a 13Ă performance advantage over the original RePast implementation
Parallel pair-wise interaction for multi-agent immune systems modelling
Agent Based Modelling (ABM), is an approach for modelling dynamic systems and studying complex and emergent behaviour. ABM approach is a very common technique in biological domain due to high demand for a large scale analysis tool to collect and interpret information to solve biological problems. However, simulating large scale cellular level models (i.e. large number of agents/entities) require a high degree of computational power which is achievable through parallel computing methods such as Graphics Processing Units (GPUs). The use of parallel approaches in ABMs is growing rapidly specifically when modelling in continuous space system (particle based). Parallel implementation of particle based simulation within continuum space where agents contain quantities of chemicals/substances is very challenging. Pair-wise interactions are different abstraction to continuous space (particle) models which is commonly used for immune system modelling. This paper describes an approach to parallelising the key component of biological and immune system models (pair-wise interactions) within an ABM model. Our performance results demonstrate the applicability of this method to a broader class of biological systems with the same type of cell interactions and that it can be used as the basis for developing complete immune system models on parallel hardware
Parallelisation strategies for agent based simulation of immune systems
Background
In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches.
Results
This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment.
Conclusions
Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware
Recommended from our members
Proceedings of the Workshop on Membrane Computing, WMC 2016.
yesThis Workshop on Membrane Computing, at the Conference of Unconventional
Computation and Natural Computation (UCNC), 12th July 2016, Manchester,
UK, is the second event of this type after the Workshop at UCNC 2015 in
Auckland, New Zealand*. Following the tradition of the 2015 Workshop the
Proceedings are published as technical report.
The Workshop consisted of one invited talk and six contributed presentations
(three full papers and three extended abstracts) covering a broad spectrum of
topics in Membrane Computing, from computational and complexity theory to
formal verification, simulation and applications in robotics. All these papers â
see below, but the last extended abstract, are included in this volume.
The invited talk given by Rudolf Freund, âP SystemsWorking in Set Modesâ,
presented a general overview on basic topics in the theory of Membrane Computing
as well as new developments and future research directions in this area.
Radu Nicolescu in âDistributed and Parallel Dynamic Programming Algorithms
Modelled on cP Systemsâ presented an interesting dynamic programming
algorithm in a distributed and parallel setting based on P systems enriched with
adequate data structure and programming concepts representation. Omar Belingheri,
Antonio E. Porreca and Claudio Zandron showed in âP Systems with
Hybrid Setsâ that P systems with negative multiplicities of objects are less powerful
than Turing machines. Artiom Alhazov, Rudolf Freund and Sergiu Ivanov
presented in âExtended Spiking Neural P Systems with Statesâ new results regading
the newly introduced topic of spiking neural P systems where states are
considered.
âSelection Criteria for Statistical Model Checkerâ, by Mehmet E. Bakir and
Mike Stannett, presented some early experiments in selecting adequate statistical
model checkers for biological systems modelled with P systems. In âTowards
Agent-Based Simulation of Kernel P Systems using FLAME and FLAME GPUâ,
Raluca Lefticaru, Luis F. MacĂas-Ramos, IonuĆŁ M. Niculescu, LaurenĆŁiu MierlÄ
presented some of the advatages of implementing kernel P systems simulations in
FLAME. Andrei G. Florea and CÄtÄlin Buiu, in âAn Efficient Implementation and Integration of a P Colony Simulator for Swarm Robotics Applications" presented an interesting and efficient implementation based on P colonies for swarms of Kilobot robots.
*http://ucnc15.wordpress.fos.auckland.ac.nz/workshop-on-membrane-computingwmc-
at-the-conference-on-unconventional-computation-natural-computation
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