Automatic Algorithm Selection For Complex Simulation Problems
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Automatic Algorithm Selection for Complex Simulation Problems
Author: Roland Ewald
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-11-20
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. Roland Ewald analyzes and discusses existing approaches to solve the algorithm selection problem in the context of simulation. He introduces a framework for automatic simulation algorithm selection and describes its integration into the open-source modelling and simulation framework James II. Its selection mechanisms are able to cope with three situations: no prior knowledge is available, the impact of problem features on simulator performance is unknown, and a relationship between problem features and algorithm performance can be established empirically. The author concludes with an experimental evaluation of the developed methods.
Hypothesis-Driven Simulation Studies
Fabian Lorig develops a procedure model for hypothesis-driven simulation studies which supports the design, conducting, and analysis of simulation experiments. It is aimed at facilitating the execution of simulation studies with regard to the replicability and reproducibility of the results. In comparison to existing models, this approach is based on a formally specified hypothesis. Each step of the simulation study can be adapted to the central hypothesis and performed in such a way that it can optimally contribute to the verification and thus to the confirmation or rejection of the hypothesis.
Identifying and Harnessing Concurrency for Parallel and Distributed Network Simulation
Author: Andelfinger, Philipp Josef
language: en
Publisher: KIT Scientific Publishing
Release Date: 2016-07-28
Although computer networks are inherently parallel systems, the parallel execution of network simulations on interconnected processors frequently yields only limited benefits. In this thesis, methods are proposed to estimate and understand the parallelization potential of network simulations. Further, mechanisms and architectures for exploiting the massively parallel processing resources of modern graphics cards to accelerate network simulations are proposed and evaluated.