2,718 research outputs found
Parallel Implementations of Cellular Automata for Traffic Models
The Biham-Middleton-Levine (BML) traffic model is a simple two-dimensional,
discrete Cellular Automaton (CA) that has been used to study self-organization
and phase transitions arising in traffic flows. From the computational point of
view, the BML model exhibits the usual features of discrete CA, where the state
of the automaton are updated according to simple rules that depend on the state
of each cell and its neighbors. In this paper we study the impact of various
optimizations for speeding up CA computations by using the BML model as a case
study. In particular, we describe and analyze the impact of several parallel
implementations that rely on CPU features, such as multiple cores or SIMD
instructions, and on GPUs. Experimental evaluation provides quantitative
measures of the payoff of each technique in terms of speedup with respect to a
plain serial implementation. Our findings show that the performance gap between
CPU and GPU implementations of the BML traffic model can be reduced by clever
exploitation of all CPU features
An AER Spike-Processing Filter Simulator and Automatic VHDL Generator Based on Cellular Automata
Spike-based systems are neuro-inspired circuits implementations
traditionally used for sensory systems or sensor signal processing. Address-Event-
Representation (AER) is a neuromorphic communication protocol for transferring
asynchronous events between VLSI spike-based chips. These neuro-inspired
implementations allow developing complex, multilayer, multichip neuromorphic
systems and have been used to design sensor chips, such as retinas and cochlea,
processing chips, e.g. filters, and learning chips. Furthermore, Cellular Automata
(CA) is a bio-inspired processing model for problem solving. This approach
divides the processing synchronous cells which change their states at the same time
in order to get the solution. This paper presents a software simulator able to gather
several spike-based elements into the same workspace in order to test a CA
architecture based on AER before a hardware implementation. Furthermore this
simulator produces VHDL for testing the AER-CA into the FPGA of the USBAER
AER-tool.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
Developing Efficient Discrete Simulations on Multicore and GPU Architectures
In this paper we show how to efficiently implement parallel discrete simulations on multicoreandGPUarchitecturesthrougharealexampleofanapplication: acellularautomatamodel of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with 48 cores, and also an NVIDIA Tesla V100GPU,bothrunningonAmazonWebServer(AWS)Cloud;and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results were compared and analyzed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current multicore CPUs with large core numbers can bring a performance very near to that of GPUs, and even identical in some cases.Ministerio de Economía, Industria y Competitividad, Gobierno de España (MINECO), and the Agencia Estatal de Investigación (AEI) of Spain, cofinanced by FEDER funds (EU) TIN2017-89842
Cellular Automata Models of Road Traffic
In this paper, we give an elaborate and understandable review of traffic
cellular automata (TCA) models, which are a class of computationally efficient
microscopic traffic flow models. TCA models arise from the physics discipline
of statistical mechanics, having the goal of reproducing the correct
macroscopic behaviour based on a minimal description of microscopic
interactions. After giving an overview of cellular automata (CA) models, their
background and physical setup, we introduce the mathematical notations, show
how to perform measurements on a TCA model's lattice of cells, as well as how
to convert these quantities into real-world units and vice versa. The majority
of this paper then relays an extensive account of the behavioural aspects of
several TCA models encountered in literature. Already, several reviews of TCA
models exist, but none of them consider all the models exclusively from the
behavioural point of view. In this respect, our overview fills this void, as it
focusses on the behaviour of the TCA models, by means of time-space and
phase-space diagrams, and histograms showing the distributions of vehicles'
speeds, space, and time gaps. In the report, we subsequently give a concise
overview of TCA models that are employed in a multi-lane setting, and some of
the TCA models used to describe city traffic as a two-dimensional grid of
cells, or as a road network with explicitly modelled intersections. The final
part of the paper illustrates some of the more common analytical approximations
to single-cell TCA models.Comment: Accepted for publication in "Physics Reports". A version of this
paper with high-quality images can be found at: http://phdsven.dyns.cx (go to
"Papers written"
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
Fast Low Fidelity Microsimulation of Vehicle Traffic on Supercomputers
A set of very simple rules for driving behavior used to simulate roadway
traffic gives realistic results. Because of its simplicity, it is easy to
implement the model on supercomputers (vectorizing and parallel), where we have
achieved real time limits of more than 4~million~kilometers (or more than
53~million vehicle sec/sec). The model can be used for applications where both
high simulation speed and individual vehicle resolution are needed. We use the
model for extended statistical analysis to gain insight into traffic phenomena
near capacity, and we discuss that this model is a good candidate for network
routing applications. (Submitted to Transportation Research Board Meeting, Jan.
1994, Washington D.C.)Comment: 11 pages, latex, figs. available upon request, Cologne-WP 93.14
Preliminary Results of a Multiagent Traffic Simulation for Berlin
This paper provides an introduction to multi-agent traffic simulation. Metropolitan regions can consist of several million inhabitants, implying the simulation of several million travelers, which represents a considerable computational challenge. We reports on our recent case study of a real-world Berlin scenario. The paper explains computational techniques necessary to achieve results. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the re-unification, are considerably larger than in previous scenarios that we have treated
Parallel implementation of the TRANSIMS micro-simulation
This paper describes the parallel implementation of the TRANSIMS traffic
micro-simulation. The parallelization method is domain decomposition, which
means that each CPU of the parallel computer is responsible for a different
geographical area of the simulated region. We describe how information between
domains is exchanged, and how the transportation network graph is partitioned.
An adaptive scheme is used to optimize load balancing. We then demonstrate how
computing speeds of our parallel micro-simulations can be systematically
predicted once the scenario and the computer architecture are known. This makes
it possible, for example, to decide if a certain study is feasible with a
certain computing budget, and how to invest that budget. The main ingredients
of the prediction are knowledge about the parallel implementation of the
micro-simulation, knowledge about the characteristics of the partitioning of
the transportation network graph, and knowledge about the interaction of these
quantities with the computer system. In particular, we investigate the
differences between switched and non-switched topologies, and the effects of 10
Mbit, 100 Mbit, and Gbit Ethernet. keywords: Traffic simulation, parallel
computing, transportation planning, TRANSIM
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