Parallel and Distributed Spatial Simulation of Chemical Reactions

Jeschke, Matthias; Park, Alfred; Ewald, Roland; Fujimoto, Richard M.; Uhrmacher, Adelinde M.

doi:10.1109/pads.2008.20

Cited by 22 publications

(16 citation statements)

References 22 publications

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“…An example for such a kind of model are the chemical reaction networks discussed in section 9 (p. 177): reaction times are exponentially distributed and hence time delays may be arbitrarily close to 0. The efficient execution of such kinds of models is very challenging (e.g., [160]). Finally, a lookahead is not necessarily fixed during the execution of a simulation run -however, a fixed lookahead is assumed in the following.…”

Section: Algorithms Under Considerationmentioning

confidence: 99%

Automatic Algorithm Selection for Complex Simulation Problems

Ewald¹

2012

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To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments, without demanding expert knowledge on simulation.The first part of the thesis surveys existing approaches to solve the algorithm selection problem and discusses techniques to analyze simulation algorithm performance. A unified categorization of existing algorithm selection techniques is worked out, as these stem from various research domains (e.g., finance, artificial intelligence).The second part introduces a software framework for automatic simulation algorithm selection and describes its constituents, as well as their integration into the modeling and simulation framework JAMES II. The implemented selection mechanisms are able to cope with three situations: a) no prior knowledge is available, b) the impact of problem features on performance is unknown, and c) a relationship between problem features and algorithm performance can be established empirically.An experimental evaluation of the developed methods concludes the thesis. It is shown that an automated algorithm selection may significantly increase the overall performance of a simulation system. Some of the presented mechanisms also support the research on simulation methods, as they facilitate their development and evaluation. ZusammenfassungDie Auswahl des passendsten Simulationsalgorithmus für eine bestimmte Aufgabe ist oftmals schwierig. Dies liegt an der komplexen Interaktion zwischen Modelleigenschaften, Implementierungsdetails und der Laufzeitumgebung, welche die Gesamtleistung stark beeinflussen kann. Eine automatisierte Auswahl von Simulationsalgorithmen unterstützt die Anwender eines Simulationssystems, indem sie eine geeignete Konfiguration des Systems ohne zusätzliches Fachwissen aus dem Bereich Simulation ermöglicht.Der erste Teil der Arbeit befasst sich eingehend mit Vorarbeiten zur automatischen Algorithmenauswahl, sowie mit der Leistungsanalyse von Simulationsalgorithmen. Eine einheitliche Kategorisierung wird erarbeitet um die aus den verschiedensten Fachgebieten (z.B. Finanzwesen, Künstliche Intelligenz) stammenden Ansätze zur Algorithmenauswahl einzuordnen.Der zweite Teil der Arbeit stellt ein Rahmenwerk zur automatischen Auswahl von Simulationsalgorithmen vor und beschreibt dessen Bestandteile, einschließlich ihrer Integration ins Modellierungsund Simulationsrahmenwerk JAMES II. Die realisierten Auswahlmechanismen beziehen sich dabei auf drei Situationen: a) keinerlei Vorwissen ist vorhanden, b) der Einfluss von Problemeigenschaften auf die Leistung ist unbekannt, und c) ein Zusammenhang zwischen Problemeigenschaften und Algorithmenleistung konnte empirisch ermittelt werden.Eine experimentelle Untersuchung der entwickelten Methoden schließt die Arbeit ab. Es wird gezeigt, dass eine aut...

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Section: Algorithms Under Considerationmentioning

confidence: 99%

Automatic Algorithm Selection for Complex Simulation Problems

Ewald¹

2012

Self Cite

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“…[25] presents an experimental analysis of several optimistic protocols for the parallel simulation of a reaction-diffusion system. [13] uses NSM in an optimistic simulation along with an adaptive time window. [12] compares the performance of spatial τ -leaping with that of NSM and Gillespie's Multi-particle Method (GMPM) in terms of speedup and accuracy.…”

Section: Introductionmentioning

confidence: 99%

NTW-Mt

Lin

Tropper

Patoary

et al. 2015

Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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This paper describes a parallel discrete event simulator, Neuron Time Warp-Multi Thread (NTW-MT), developed for the simulation of reaction diffusion models of neurons. The simulator was developed as part of the NEURON project and is intended to be included in NEURON. It relies upon a stochastic discrete event model developed for chemical reactions. NTW-MT is optimistic and thread-based, in which communication latency among threads within the same process is minimized by pointers. We investigate the performance of NTW-MT on a reaction-diffusion model for the transmission of calcium waves in a neuron. Calcium plays a fundamental role in the second messenger system of a neuron. However, the mechanism by which calcium waves are transmitted is not entirely understood. Stochastic models are more realistic than deterministic models for small populations of ions such as those found in apical dendrites. To be more precise, we simulate a stochastic discrete event model for calcium wave propagation on an unbranched apical dendrite of a hippocampal pyramidal neuron. We examine the performance of NTW-MT on this calcium wave model and compare it to the performance of (1) a process based op- * This author is affiliated with State timistic simulator and (2) a threaded simulator which uses a single priority (SQ) queue for each thread. Our multithreaded simulator is shown to achieve superior performance to these simulators.

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“…For sampling the RDME, several methods exist, to those belong, e.g., the Next Sub-volume Method (NSM) [21] and the Gillespie Multi-Particle Method (GMP) [22]. To increase their efficiency, parallel approaches have been developed, e.g., [23], [24], [25], [26].…”

Section: Introductionmentioning

confidence: 99%

“…Due to data dependencies between partitions and additional constraints (processing events in time stamp order) it is rather difficult for fine-grained parallel simulation to achieve a speedup in a gridinspired environment [23], [29], [30], [25]. However, it appears as a natural fit for simulation replication and model parameter optimization, as those exhibit little or no data dependencies between portions of work [16].…”

Section: Introductionmentioning

confidence: 99%

Performance Issues in Evaluating Models and Designing Simulation Algorithms

Ewald

Himmelspach

Jeschke

et al. 2009

2009 International Workshop on High Performance Computational Systems Biology

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The increase and diversity of simulation methods bears witness of the need for more efficient discrete event simulations in computational biology -but how efficient are those methods, and how to ensure an efficient simulation for a concrete model?As the performance of simulation methods depends on the model, the simulator, and the infrastructure, general answers to those questions are likely to remain illusive; they have to be sought individually and experimentally instead. This requires configurable implementations of many algorithms, means to define and conduct meaningful experiments on them, and mechanisms for storing and analyzing observed performance data.In this paper, we first overview basic approaches for improving simulation performance and illustrate the challenges when comparing different methods. We then argue that providing all the aforementioned components in a single tool, in our case the open source modeling and simulation framework JAMES II, reveals many synergies in effectively pursuing both questions. This is exemplified by presenting results of recent studies and introducing a new component to swiftly evaluate simulator code changes against previous experimental data.

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Parallel and Distributed Spatial Simulation of Chemical Reactions

Cited by 22 publications

References 22 publications

Automatic Algorithm Selection for Complex Simulation Problems

Automatic Algorithm Selection for Complex Simulation Problems

NTW-Mt

Performance Issues in Evaluating Models and Designing Simulation Algorithms

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