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The time dependent propensity function for acceleration of spatial stochastic simulation of reaction-diffusion systems

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JOURNAL OF COMPUTATIONAL PHYSICS 2014年 274卷 524-549页

作者： Fu, Jin Wu, Sheng Li, Hong Petzold, Linda R. Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Teradata Inc El Segundo CA USA

The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy. (C) 2014 Elsevier Inc. All rights reserved.

关键词： spatial stochastic simulation Reaction-diffusion Time dependent propensity

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Abstract Next Subvolume Method: A logical process-based approach for spatial stochastic simulation of chemical reactions

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2011年第3期35卷 193-198页

作者： Wang, Bing Hou, Bonan Xing, Fei Yao, Yiping Natl Univ Def Technol Sch Comp Sci Changsha 410073 Hunan Peoples R China

The spatial stochastic simulation of biochemical systems requires significant calculation efforts. Parallel discrete-event simulation is a promising approach to accelerate the execution of simulation runs. However, achievable speedup depends on the parallelism inherent in the model. One of our goals is to explore this degree of parallelism in the Next Subvolume Method type simulations. Therefore we introduce the Abstract Next Subvolume Method, in which we decouple the model representation from the sequential simulation algorithms, and prove that state trajectories generated by its executions statistically accord with those generated by the Next Subvolume Method. The experimental performance analysis shows that optimistic synchronization algorithms, together with careful controls over the speculative execution, are necessary to achieve considerable speedup and scalability in parallel spatial stochastic simulation of chemical reactions. Our proposed method facilitates a flexible incorporation of different synchronization algorithms, and can be used to select the proper synchronization algorithm to achieve the efficient parallel simulation of chemical reactions. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： stochastic simulation algorithm Next subvolume Method spatial stochastic simulation Parallel discrete-event simulation

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Unveiling human-assisted dispersal mechanisms in invasive alien insects: Integration of spatial stochastic simulation and phenology models

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ECOLOGICAL MODELLING 2010年第17期221卷 2068-2075页

作者： Carrasco, L. R. Mumford, J. D. MacLeod, A. Harwood, T. Grabenweger, G. Leach, A. W. Knight, J. D. Baker, R. H. A. Univ London Imperial Coll Sci Technol & Med Ctr Environm Policy London SW7 2AZ England Food & Environm Res Agcy York YO41 1LZ N Yorkshire England CSIRO Entomol Canberra ACT 2601 Australia Austrian Agcy Hlth & Food Safety Inst Plant Hlth A-1220 Vienna Austria

Capturing the spread of biological invasions in heterogeneous landscapes is a complex modelling task where information on both dispersal and population dynamics needs to be integrated. spatial stochastic simulation and phenology models have rarely been combined to assist in the study of human-assisted long-distance dispersal events. Here we develop a process-based spatially explicit landscape-extent simulation model that considers the spread and detection of invasive insects. Natural and human-assisted dispersal mechanisms are modelled with an individual-based approach using negative exponential and negative power law dispersal kernels and gravity models. The model incorporates a phenology sub-model that uses daily temperature grids for the prediction and timing of the population dynamics in each habitat patch. The model was applied to the study of the invasion by the important maize pest western corn rootworm (WCR) Diabrotica virgifera ssp. virgifera in Europe. We parameterized and validated the model using maximum likelihood and simulation methods from the historical invasion of WCR in Austria. WCR was found to follow stratified dispersal where international transport networks in the Danube basin played a key role in the occurrence of long-distance dispersal events. Detection measures were found to be effective and altitude had a significant effect on limiting the spread of WCR. spatial stochastic simulation combined with phenology models, maximum likelihood methods and predicted versus observed regression showed a high degree of flexibility that captured the salient features of WCR spread in Austria. This modelling approach is useful because it allows to fully exploit and the often limited and heterogeneous information available regarding the population dynamics and dispersal of alien invasive insects. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Biological invasions Biosecurity Pest risk analysis Policy analysis spatial stochastic simulation

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Exposing Inter-Process Information for Efficient Parallel Discrete Event simulation of spatial stochastic Systems 17

Exposing Inter-Process Information for Efficient Parallel Di...

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5th ACM SIGSIM Conference on Principles of Advanced Discrete simulation (SIGSIM-PADS)

作者： Linden, Jonatan Bauer, Pavol Engblom, Stefan Jonsson, Bengt Uppsala Univ Dept Informat Technol Uppsala Sweden

ISBN: (纸本)9781450344890

We present a new efficient approach to the parallelization of discrete event simulators for multicore computers, which is based on exposing and disseminating essential information between processors. We aim specifically at simulation models with a spatial structure, where time intervals between successive events are highly variable and without lower bounds. In Parallel Discrete Event simulation (PDES), the model is distributed onto parallel processes. A key challenge in PDES is that each process must continuously decide when to pause its local simulation in order to reduce the risk of expensive rollbacks caused by future "delayed" incoming events from other processes. A process could make such decisions optimally if it would know the timestamps of future incoming events. Unfortunately, this information is often not available in PDES algorithms. We present an approach to designing efficient PDES algorithms, in which an existing natural parallelization of PDES is restructured in order to expose and disseminate more precise information about future incoming events to each LP. We have implemented our approach in a parallel simulator for spatially extended Markovian processes, intended for simulating, e.g., chemical reactions, biological and epidemiological processes. On 32 cores, our implementation exhibits speedup that significantly outweighs the overhead incurred by the refinement. We also show that our resulting simulator is superior in performance to existing simulators for comparable models, achieving for 32 cores an average speedup of 20 relative to an efficient sequential implementation.

关键词： Parallel Discrete-Event simulation PDES Optimism control Multicore spatial stochastic simulation

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MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE spatial stochastic COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2016年第3期38卷 C179-C202页

作者： Drawert, Brian Trogdon, Michael Toor, Salman Petzold, Linda Hellander, Andreas Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Univ Calif Santa Barbara Dept Mech Engn Santa Barbara CA 93106 USA Univ Helsinki Dept Comp Sci FI-00014 Helsinki Finland Uppsala Univ Div Comp Sci Dept Informat Technol S-75105 Uppsala Sweden

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

关键词： simulation software spatial stochastic simulation systems biology computational experiments cloud computing

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Simulating the Spread of an Invasive Termite in an Urban Environment Using a stochastic Individual-Based Model

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ENVIRONMENTAL ENTOMOLOGY 2013年第3期42卷 412-423页

作者： Tonini, Francesco Hochmair, Hartwig H. Scheffrahn, Rudolf H. Deangelis, Donald L. Univ Florida Ft Lauderdale Res & Educ Ctr Davie FL 33314 USA Univ Miami Dept Biol Coral Gables FL 33124 USA

Invasive termites are destructive insect pests that cause billions of dollars in property damage every year. Termite species can be transported overseas by maritime vessels. However, only if the climatic conditions are suitable will the introduced species flourish. Models predicting the areas of infestation following initial introduction of an invasive species could help regulatory agencies develop successful early detection, quarantine, or eradication efforts. At present, no model has been developed to estimate the geographic spread of a termite infestation from a set of surveyed locations. In the current study, we used actual field data as a starting point, and relevant information on termite species to develop a spatially-explicit stochastic individual-based simulation to predict areas potentially infested by an invasive termite, Nasutitermes corniger (Motschulsky), in Dania Beach, FL. The Monte Carlo technique is used to assess outcome uncertainty. A set of model realizations describing potential areas of infestation were considered in a sensitivity analysis, which showed that the model results had greatest sensitivity to number of alates released from nest, alate survival, maximum pheromone attraction distance between heterosexual pairs, and mean flight distance. Results showed that the areas predicted as infested in all simulation runs of a baseline model cover the spatial extent of all locations recently discovered. The model presented in this study could be applied to any invasive termite species after proper calibration of parameters. The simulation herein can be used by regulatory authorities to define most probable quarantine and survey zones.

关键词： Monte Carlo simulation invasive species individual-based approach spatial stochastic simulation habitat suitability

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Efficient inter-process synchronization for parallel discrete event simulation on multicores 15

Efficient inter-process synchronization for parallel discret...

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3rd ACM Conference on SIGSIM-Principles of Advanced Discrete simulation, SIGSIM-PADS 2015

作者： Bauer, Pavol Lindén, Jonatan Engblom, Stefan Jonsson, Bengt Dept. of Information Technology Uppsala University Sweden

ISBN: (纸本)9781450335836

We present a new technique for controlling optimism in Parallel Discrete Event simulation on multicores. It is designed to be suitable for simulating models, in which the time intervals between successive events between different processes are highly variable, and have no lower bounds. In our technique, called Dynamic Local Time Window Estimates (DLTWE), each processor communicates time estimates of its next inter-processor event to (some of) its neighbors, which use the estimates as bounds for advancement of their local simulation time. We have implemented our technique in a parallel simulator for simulation of spatially extended Markovian processes of interacting entities, which can model chemical reactions, processes from biology, epidemics, and many other applications. Intervals between successive events are exponentially distributed, thus having a signifgicant variance and no lower bound. We show that the DLTWE technique can be tuned to drastically reduce the frequency of rollbacks and enable speedups which is superior to that obtained by other works. We also show that the DLTWE technique significantly improves performance over other existing techniques for optimism control that attempt to predict arrival of inter-process events by statistical techniques. © 2015 ACM.

关键词： pdes spatial stochastic simulation multicore parallel discrete-event simulation optimism control

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Exposing Inter-process Information for Efficient PDES of spatial stochastic Systems on Multicores

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ACM TRANSACTIONS ON MODELING AND COMPUTER simulation 2019年第2期29卷 11-11页

作者： Linden, Jonatan Bauer, Pavol Engblom, Stefan Jonsson, Bengt Uppsala Univ Dept Informat Technol Uppsala Sweden German Ctr Neurodegenerat Dis Neural Networks Bonn Germany

We present a new approach for efficient process synchronization in parallel discrete event simulation on multicore computers. We aim specifically at simulation of spatially extended stochastic system models where time intervals between successive inter-process events are highly variable and without lower bounds: This includes models governed by the mesoscopic Reaction-Diffusion Master Equation (RDME). A central part of our approach is a mechanism for optimism control, in which each process disseminates accurate information about timestamps of its future outgoing interprocess events to its neighbours. This information gives each process a precise basis for deciding when to pause local processing to reduce the risk of expensive rollbacks caused by future "delayed" incoming events. We apply our approach to a natural parallelization of the Next Subvolume Method (NSM) for simulating systems obeying RDME. Since this natural parallelization does not expose accurate timestamps of future interprocess events, we restructure it to expose such information, resulting in a simulation algorithm called Refined Parallel NSM (Refined PNSM). We have implemented Refined PNSM in a parallel simulator for spatial extended Markovian processes. On 32 cores, it achieves an efficiency ranging between 43-95% for large models, and on average 37% for small models, compared to an efficient sequential simulation without any code for parallelization. It is shown that the gain of restructuring the naive parallelization into Refined PNSM more than outweighs its overhead. We also show that our resulting simulator is superior in performance to existing simulators on multicores for comparable models.

关键词： Parallel discrete-event simulation PDES optimism control multicore spatial stochastic simulation

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Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh and its implementation in STEPS

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2013年第2013期7卷 129-129页

作者： Hepburn, Iain Cannon, Robert De Schutter, Erik Okinawa Inst Sci & Technol Computat Neurosci Unit Onna Son Okinawa 9040495 Japan Univ Antwerp B-2020 Antwerp Belgium Textensor Ltd Edinburgh Midlothian Scotland

We describe a novel method for calculating the quasi-static electrical potential on tetrahedral meshes, which we call E-Field. The E-Field method is implemented in STEPS, which performs stochastic spatial reaction-diffusion computations in tetrahedral-based cellular geometry reconstructions. This provides a level of integration between electrical excitability and spatial molecular dynamics in realistic cellular morphology not previously achievable. Deterministic solutions are also possible. By performing the Rallpack tests we demonstrate the accuracy of the E-Field method. Efficient node ordering is an important practical consideration, and we find that a breadth-first search provides the best solutions, although principal axis ordering suffices for some geometries. We discuss potential applications and possible future directions, and predict that the E-Field implementation in STEPS will play an important role in the future of multiscale neural simulations.

关键词： 3D electrical potential tetrahedral meshes membrane potential complex morphology spatial stochastic simulation multiscale simulation efficient node ordering

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