A Multi-threaded Execution Model for the Agent-Based SEMSim Traffic Simulation

Aydt, Heiko; Xu, Yadong; Lees, Michael; Knoll, Alois

doi:10.1007/978-3-642-45037-2_1

Cited by 21 publications

(17 citation statements)

References 13 publications

(12 reference statements)

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“…This consists of a shared memory multi-threaded simulation execution core as introduced in [27]. This offers an efficient way to handle a large number of agents on a multi-core system.…”

Section: Semsim Trafficmentioning

confidence: 99%

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SEMSim Cloud Service: Large-scale urban systems simulation in the cloud

Zehe¹,

Knoll

Cai

et al. 2015

Simulation Modelling Practice and Theory

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a b s t r a c tLarge-scale urban systems simulations are complex and with a large number of active simulation entities the computational workload is extensive. Workstation computers have only limited capabilities of delivering results for large-scale simulations. This leads to the problem that many researchers and engineers have to either reduce the scope of their experiments or fail to execute as many experiments as they would like in a given time frame. The use of high-performance computing (HPC) infrastructure offers a solution to the problem. Users of such simulations are often domain experts with no or little experience with HPC environments. In addition users do not necessarily have access to an HPC. In this paper we propose an architecture for a cloud-based urban systems simulation platform which specifically aims at making large-scale simulations available to typical users. The proposed architecture also addresses the issue of data confidentiality. In addition we describe the Scalable Electro-Mobility Simulation (SEMSim) Cloud Service that implements the proposed architecture.

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“…This consists of a shared memory multi-threaded simulation execution core as introduced in [27]. This offers an efficient way to handle a large number of agents on a multi-core system.…”

Section: Semsim Trafficmentioning

confidence: 99%

“…the simulation there are two steps as explained in [27]. The first step is to calculate a route for a newly created agent or to update the route if the agent needs re-routing.…”

Section: Semsim Trafficmentioning

confidence: 99%

SEMSim Cloud Service: Large-scale urban systems simulation in the cloud

Zehe¹,

Knoll

Cai

et al. 2015

Simulation Modelling Practice and Theory

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“…At the same time, the communication cost between processors should be minimized. For this purpose multi-core parallel algorithms and data structures have been developed in [8,9] and Message Passing Interface (MPI)-based communication among CPU clusters is used in [2]. In summary, traditional parallel computing using either CPU clusters or multi-core processors is the main-stream technology to support large-scale mesoscopic traffic simulations.…”

Section: Introductionmentioning

confidence: 99%

“…They have used multi-core CPUs or CPU clusters to handle the large computational load [7][8][9]. Traffic network is typically decomposed into segments that are handled by different processors.…”

Section: Introductionmentioning

confidence: 99%

Mesoscopic traffic simulation on CPU/GPU

Tan

et al. 2014

Proceedings of the 2nd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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Mesoscopic traffic simulation is an important branch of technology to support offline large-scale simulation-based traffic planning and online simulation-based traffic management. One of the major concerns using mesoscopic traffic simulations is the performance, which means the required time to simulate a traffic scenario. At the same time, the GPU has recently been a success, because of its massive performance compared to the CPU. Thus, a critical question is "whether the GPU can be a potential highperformance platform for mesoscopic traffic simulations?" To the best of our knowledge, there is no clear answer in the research area. In this paper, we firstly propose a comprehensive framework to run a traditional time-stepped mesoscopic traffic simulation on CPU/GPU. Then, we design a boundary processing method to guarantee the correctness of running mesoscopic supply traffic simulations on the GPU. Thirdly, the proposed mesoscopic traffic simulation framework is demonstrated to simulate 100,000 vehicles moving on a large-scale grid road network. In this case study, running a mesoscopic supply traffic simulation on a GPU (GeForce GT 650M) gives 11.2 times speedup, compared with running the same supply simulation on a CPU core (Intel E5-2620). In the end, this paper explains the theoretical limitation of running mesoscopic supply traffic simulations on the GPU. In conclusion, regardless of high system complexity, the proposed mesoscopic traffic simulation framework on CPU/GPU provides an innovative and promising solution for high-performance mesoscopic traffic simulations.

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“…Conservative strategies prohibits any causality error from occurring, whereas optimistic strategies uses a detection and recover approach: causality errors are detected, and a rollback mechanism is invoked to recover [7]. In existing work, synchronization in the traffic simulations is generally achieved using global barriers either in a shared memory environment [1,2] or distributed memory environment [3,17,26]. This method is straightforward to use.…”

Section: Introductionmentioning

confidence: 99%

An Asynchronous Synchronization Strategy for Parallel Large-scale Agent-based Traffic Simulations

Cai

Aydt³

et al. 2015

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

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Large-scale agent-based traffic simulation is a promising tool to study the road traffic and help solving traffic problems, such as congestion and high emission in megacities. Such simulation requires high computational resource which triggers the need for parallel computing. The parallelization of agent-based traffic simulations is generally performed by decomposing the simulation space into spatial subregions. The agent models contained by each subregion are executed by Logical Processes (LPs). As the simulated system evolves over the simulation time in individual LPs, synchronization among LPs is required due to data dependencies. Existing work has used global barriers for synchronization which is a type of synchronous synchronization method. However, global barriers have very low efficiency due to the waiting of processes at barriers. High synchronization overhead is still one of the major performance issues in parallel large-scale agent-based traffic simulations. In this paper, we proposed a novel asynchronous conservative synchronization strategy named Mutual Appointment (MA) to address this issue. MA removes global barriers and allows LPs to communicate individually. Since the efficiency of conservative synchronization relies on the lookahead of the simulated system, a heuristic was developed to increase the lookahead in agentbased traffic simulations. It takes advantage of the intrinsic uncertainties in traffic simulations. MA together with the lookahead heuristic forms the Relaxed Mutual Appointment (RMA) strategy. Its efficiency was investigated in the parallel agent-based traffic simulator SEMSim Traffic using real world traffic data. Experiment results showed that the MA Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. strategy improved the speed-up of the parallel simulation compared to the barrier method, and the RMA strategy further improved the MA strategy by reducing the number of synchronization messages significantly.

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A Multi-threaded Execution Model for the Agent-Based SEMSim Traffic Simulation

Cited by 21 publications

References 13 publications

SEMSim Cloud Service: Large-scale urban systems simulation in the cloud

SEMSim Cloud Service: Large-scale urban systems simulation in the cloud

Mesoscopic traffic simulation on CPU/GPU

An Asynchronous Synchronization Strategy for Parallel Large-scale Agent-based Traffic Simulations

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