Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Performance Analysis for Large-Scale Parallel Microscopic Traffic Simulation System

View through CrossRef
PMTS (Parallel Microscopic Traffic Simulation) is a large-scale microscopic traffic network simulation system using a real traffic network of Shanghai, China. It describes traffic events in object oriented mode, uses MPI (Message Passing Interface) as communicate middleware, partitions the simulation traffic network into subnetworks, and runs simulations of subnetworks concurrently on a cluster of processors connected by high-speed Ethernet. To optimize the performance of PMTS, a number of challenges need to be solved, including traffic network partition, subnetworks communication and synchronization, and workload balance. The parallelization of PMTS is domain decomposition, which means that the geographical region for simulation is decomposed into several domains of similar size and each processor of the PC cluster is responsible for a different geographical area. The partition method needs to fulfill two conditions to make it efficient: minimize the communication overhead and partition the subnetworks of the equal computation load. The goals of maximizing the parallelization and minimizing the communication overhead is conflict, and the tradeoff should be made. PVTM simulates the vehicles activities on the traffic network, and the computation overhead for one vehicle is certain. The time overhead on one processor depends on the number of vehicles for simulation. The individual simulation of vehicle activity includes: vehicle generation, vehicle running, and the vehicle going through the boundary zone. The derivation of the predictive performance is demonstrated and the calculation of the time cost for one processor with a certain number of vehicles is provided. In order to be efficient, the load on different processors should be as similar as possible. The load depends on the actual vehicle number on the respective processor that also varies as the simulation run. In each time step, the workload is collected for each processor. For the next iteration, the information will be fed back to the load-balancer, which will move the boundary of the grid to balance the vehicle numbers among the domains. The performance metrics of the simulation system mainly depend on the load balance, scalability, speedup, and efficiency. The performance optimization of PMTS has been proved to be effective when it is put into use and the experiment results are also provided which match the analysis.
Title: Performance Analysis for Large-Scale Parallel Microscopic Traffic Simulation System
Description:
PMTS (Parallel Microscopic Traffic Simulation) is a large-scale microscopic traffic network simulation system using a real traffic network of Shanghai, China.
It describes traffic events in object oriented mode, uses MPI (Message Passing Interface) as communicate middleware, partitions the simulation traffic network into subnetworks, and runs simulations of subnetworks concurrently on a cluster of processors connected by high-speed Ethernet.
To optimize the performance of PMTS, a number of challenges need to be solved, including traffic network partition, subnetworks communication and synchronization, and workload balance.
The parallelization of PMTS is domain decomposition, which means that the geographical region for simulation is decomposed into several domains of similar size and each processor of the PC cluster is responsible for a different geographical area.
The partition method needs to fulfill two conditions to make it efficient: minimize the communication overhead and partition the subnetworks of the equal computation load.
The goals of maximizing the parallelization and minimizing the communication overhead is conflict, and the tradeoff should be made.
PVTM simulates the vehicles activities on the traffic network, and the computation overhead for one vehicle is certain.
The time overhead on one processor depends on the number of vehicles for simulation.
The individual simulation of vehicle activity includes: vehicle generation, vehicle running, and the vehicle going through the boundary zone.
The derivation of the predictive performance is demonstrated and the calculation of the time cost for one processor with a certain number of vehicles is provided.
In order to be efficient, the load on different processors should be as similar as possible.
The load depends on the actual vehicle number on the respective processor that also varies as the simulation run.
In each time step, the workload is collected for each processor.
For the next iteration, the information will be fed back to the load-balancer, which will move the boundary of the grid to balance the vehicle numbers among the domains.
The performance metrics of the simulation system mainly depend on the load balance, scalability, speedup, and efficiency.
The performance optimization of PMTS has been proved to be effective when it is put into use and the experiment results are also provided which match the analysis.

Related Results

The Burden of Road Traffic Injuries: A Global Perspective
The Burden of Road Traffic Injuries: A Global Perspective
Introduction     Road Traffic Injury (RTI) pose a significant health challenge. It represents the eighth leading cause of death globally, prompting the UN to designate 2011-2020 as...
ANALISIS PERTIMBANGAN MAHKAMAH AGUNG DALAM MENGABULKAN KASASI TERDAKWA (STUDI PUTUSAN NOMOR 2959/K/PID.SUS/2022)
ANALISIS PERTIMBANGAN MAHKAMAH AGUNG DALAM MENGABULKAN KASASI TERDAKWA (STUDI PUTUSAN NOMOR 2959/K/PID.SUS/2022)
<p><em><span class="markedContent"><span style="left: calc(var(--scale-factor)*195.53px); top: calc(var(--scale-factor)*496.87px); font-size: calc(var(--scale-...
KEDUDUKAN AHLI BAHASA DALAM PEMBUKTIAN PERKARA PENCEMARAN NAMA BAIK (STUDI PUTUSAN NOMOR: 47/PID.SUS/2019/PN. MGT)
KEDUDUKAN AHLI BAHASA DALAM PEMBUKTIAN PERKARA PENCEMARAN NAMA BAIK (STUDI PUTUSAN NOMOR: 47/PID.SUS/2019/PN. MGT)
<em><span id="page3R_mcid52" class="markedContent"><span style="left: calc(var(--scale-factor)*125.30px); top: calc(var(--scale-factor)*539.11px); font-size: calc(va...
Introduction to Artificial Intelligence in Traffic Systems
Introduction to Artificial Intelligence in Traffic Systems
Traffic management is a pressing challenge in modern societies. The population of humans is increasing at a substantial pace, and along with that, the expanse of urban areas and th...
Smart Traffic Control Using Computer Vision
Smart Traffic Control Using Computer Vision
A Smart Traffic Control System using Computer Vision utilizes cameras, image processing techniques, and machine learning algorithms to monitor, analyze, and manage traffic flow aut...
TYPES OF AI ALGORİTHMS USED İN TRAFFİC FLOW PREDİCTİON
TYPES OF AI ALGORİTHMS USED İN TRAFFİC FLOW PREDİCTİON
The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic...
Traffic safety outcomes of traffic law application and the adoption of new technology in traffic control
Traffic safety outcomes of traffic law application and the adoption of new technology in traffic control
Experience of the State of Qatar Introduction: Since the second half of the last decade of the twentieth century, Qatar has witnessed the implementation of a comprehensive developm...

Back to Top