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AgendaJulian Fietkau, Joachim NitschkeAgendaIntroductionConcept and ModelOverviewComputationTechnologies and Data SourcesPlans for ParallelizationSummary2 / 27


IntroductionJulian Fietkau, Joachim NitschkeProject TaskDecide on a problem that may be solved using parallel processing, andimplement a solution. → Street traffic simulationMain CaveatRealistic traffic predictions can only be made using an exceedinglydetailed model. This makes things prohibitively complicated.3 / 27


IntroductionJulian Fietkau, Joachim NitschkeProject Path1 Formulate the problem in such a way that it can be solved□ . . . using a suitably abstract model2 Solve it programmatically□ . . . on a distributed architecture4 / 27


IntroductionJulian Fietkau, Joachim NitschkeProject Goal1 Simulate thousands or millions of cars/drivers in a city2 Watch for congested and unused roads3 Optimize the road system step by step4 Visualize this process as a changing map6 / 27


Concept and Model: OverviewJulian Fietkau, Joachim NitschkeMacroscopic SimulationAbstract from single cars, traffic lights etc. to daily trafficDisplay traffic development over longer time periods and influenceson street network7 / 27


Concept and Model: OverviewJulian Fietkau, Joachim NitschkeDiscrete Simulation1 simulation step ˆ= 1 dayTraffic changes every dayChanges to street network after longer time periods8 / 27


Concept and Model: OverviewJulian Fietkau, Joachim NitschkeStreet Network9 / 27


Concept and Model: OverviewJulian Fietkau, Joachim NitschkeTripsRepresentation for a resident’s daily trafficSimplification:□ No car locomotion□ One route per day10 / 27


Concept and Model: OverviewJulian Fietkau, Joachim NitschkeTrips11 / 27


Concept and Model: ComputationJulian Fietkau, Joachim NitschkeWeightsResidents want to choose the fastest route⇒ Weights based on driving timeExpected driving timet expected =sv max12 / 27


Concept and Model: ComputationJulian Fietkau, Joachim NitschkeWeightsCars are slowed down in case of increased trafficDecrease of velocity based on number of trips using a streetIdea: Consider distribution of cars along the street and throughoutthe day13 / 27


Concept and Model: ComputationJulian Fietkau, Joachim NitschkeWeightsAs a result the weights are increased and the route becomes lessattractiveActual driving timet actual =sv actual14 / 27


Concept and Model: ComputationJulian Fietkau, Joachim NitschkeWeightsIn reality not all drivers change their route since driving time delay ispercieved differently ⇒ Perceived driving time is influenced byrandom “traffic jam sensibility”Traffic jam sensibilityt perceived = t expected + t delay · r sensibilityt delay = t actual − t expected15 / 27


Technologies and Data SourcesJulian Fietkau, Joachim NitschkeRuntime TechnologiesPythonMPI (→bonus slide)mpi4py16 / 27


Technologies and Data SourcesJulian Fietkau, Joachim NitschkeMap Data from OpenStreetMapPython and GISOSM has a nice API□ XML that includes nodes and ways17 / 27


Technologies and Data SourcesJulian Fietkau, Joachim Nitschke.........18 / 27


Technologies and Data SourcesJulian Fietkau, Joachim NitschkeDevelopment InfrastructureGitMediaWiki19 / 27


Plans for ParallelizationJulian Fietkau, Joachim NitschkeIdea for Data Decompositionnode 0 node 1 node 2node 3 node 4 node 5node 6 node 7 node 820 / 27


Plans for ParallelizationJulian Fietkau, Joachim NitschkeComputational TasksExcluding: primary preparations, visualizationShuffling with times and velocities → mostly trivialFinding the shortest paths → bulk of calculation21 / 27


Plans for ParallelizationJulian Fietkau, Joachim NitschkeDistributed Shortest PathCost-effectiveness of specialized algorithms?Balanced hierarchical networks (Antonio, Huang, Tsai)Other approaches22 / 27


Plans for ParallelizationJulian Fietkau, Joachim NitschkeBalanced hierarchical networksNetworks with hierarchically organized clustersStrategy: calculate paths along “gate nodes”Potential reduction in complexity (O(log n) possible for single path)Data distribution along cluster boundaries?23 / 27


SummaryJulian Fietkau, Joachim NitschkeMost Important PointsSimple traffic simulationMacro level with congestion analysis, street developmentMPI on PythonDistributed shortest path24 / 27


Miscellaneous: LiteratureJulian Fietkau, Joachim NitschkeLiteratureWeber, B.; Müller, P.; Wonka, P.; Gross, M.:Interactive Geometric Simulation of 4D CitiesIn: EUROGRAPHICS 28 (2009), Nr. 2Antonio, J. K.; Huang, G. M.; Tsai, W. K.:A fast distributed shortest path algorithm for a class of hierarchicallyclustered data networksIn: IEEE Trans. Comput., vol. 41, pp. 710–724, June 199225 / 27


Miscellaneous: WeblinksJulian Fietkau, Joachim NitschkeWeblinksProject wikihttp://pwiki.julian-fietkau.de/GitHub repositoryhttp://github.com/jfietkau/Streets4MPI26 / 27


Miscellaneous: Download and UsageJulian Fietkau, Joachim NitschkeDownload and UsageThese slides are published under the CC-BY-SA 3.0 license.All pictures and illustrations are based oncontent from the OpenClipArt Project.Download these slides and give feedback:http://www.julian-fietkau.de/parallel_programming_plan27 / 27

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