Optimistic Parallel Simulation over Public Resource-Computing Infrastructures and Desktop Grids

Park, A.; Fujimoto, Richard M.

doi:10.1109/ds-rt.2008.34

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…It has only been employed in master-slave simulation architectures to cope with dynamic increase/decrease of the amount of available computing resources (e.g. for simulation platforms running on top of desktop grids [19]). However, in such a context, concurrent threads operate on inherently partitioned data, while we approach multi-threading in the presence of shared data structures within the simulation-kernel process.…”

Section: Related Workmentioning

confidence: 99%

Load sharing for optimistic parallel simulations on multi core machines

Vitali

Pellegrini

Quaglia

2012

SIGMETRICS Perform. Eval. Rev.

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Parallel Discrete Event Simulation (PDES) is based on the partitioning of the simulation model into distinct Logical Processes (LPs), each one modeling a portion of the entire system, which are allowed to execute simulation events concurrently. This allows exploiting parallel computing architectures to speedup model execution, and to make very large models tractable. In this article we cope with the optimistic approach to PDES, where LPs are allowed to concurrently process their events in a speculative fashion, and rollback/recovery techniques are used to guarantee state consistency in case of causality violations along the speculative execution path. Particularly, we present an innovative load sharing approach targeted at optimizing resource usage for fruitful simulation work when running an optimistic PDES environment on top of multi-processor/multi-core machines. Beyond providing the load sharing model, we also define a load sharing oriented architectural scheme, based on a symmetric multi-threaded organization of the simulation platform. Finally, we present a real implementation of the load sharing architecture within the open source ROme OpTimistic Simulator (ROOT-Sim) package. Experimental data for an assessment of both viability and effectiveness of our proposal are presented as well.

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Section: Related Workmentioning

confidence: 99%

Load sharing for optimistic parallel simulations on multi core machines

Vitali

Pellegrini

Quaglia

2012

SIGMETRICS Perform. Eval. Rev.

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“…The usage of the optimistic approach requires some changes in the execution and logic layer in order to support the undo and restart operations. The changes are consistent with the Master/Worker paradigm, but without sophisticated optimizations [21] due to the fact that the simulator implementation should be presented in an undergraduate course.…”

Section: Structure Of Sleepmentioning

confidence: 59%

Reengineering the SLEEP simulator in a concurrent and distributed programming course

Radivojević

Cvetanović

Jovanović

2011

Comp Applic In Engineering

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This article presents laboratory exercises and project assignments developed to teach concepts of concurrent and distributed programming in Java. The exercises and assignments cover important topics determined after an analysis of topics that are often taught in different universities. During the exercises, students reengineer the general purpose discrete event simulator named SLEEP (Simulation, Logic, Execution, prEsentation, Physics). A multilayer organization of the SLEEP simulator enables coverage of the important topics in a coherent manner. In order to support parallel execution in SLEEP students start to reengineer the simulator layers with an exercise related to concurrent programming, followed by two exercises related to distributed programming, and finish with an exercise related to grid computing. The exercises follow the principles of error management training and teach students what common errors are and how to overcome them. The article also describes a practical experience with the use of SLEEP on a concurrent and distributed programming course over a 3-year period. The results show a positive impact on both students' satisfaction and performance. ß

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“…He implemented a traffic simulation platform based on GHA. Alfired Park [15], has worked on optimistic parallel simulation over public resource computing Infrastructures and Desktop Grids. He used master worker (M/W) approach for these platforms.…”

Section: Related Workmentioning

confidence: 99%

Distributed computing approach to optimize road traffic simulation

Sinha

Saini

Subramanian

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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Distributed computing is the method of running CPU intensive computations on multiple computers collectively in order to achieve a common objective. Common problems that can be solved on the distributed systems include climate/weather modeling, earthquake simulation, evolutionary computing problems and so on. These type of problems may involve billions or even trillions of computations. A single computer is not capable to finish these computations in short span of time, which is typically in days. Distributed computation helps to solve these problems in hours, which could take weeks to solve on a single computer. Distributed computing generally uses the existing resources of the organization.Traffic simulation is the process of simulating transportation systems through software on a virtual road network. Traffic simulation helps in analyzing city traffic at different time intervals of a single day. Common use cases could be analyzing city wide traffic, estimating traffic demand at a particular traffic junction and so on. This paper discusses about the approach to use distributed computing paradigm for optimizing the traffic simulations. Optimizing simulations involves running a number of traffic simulations followed by estimating the nearness of that simulation to the real available traffic data. This real data could be obtained by either manual counting at traffic junctions, or using the probes such as loop inductors, CCTV cameras etc. This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand, using evolutionary computing technique.

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Optimistic Parallel Simulation over Public Resource-Computing Infrastructures and Desktop Grids

Cited by 16 publications

References 15 publications

Load sharing for optimistic parallel simulations on multi core machines

Load sharing for optimistic parallel simulations on multi core machines

Reengineering the SLEEP simulator in a concurrent and distributed programming course

Distributed computing approach to optimize road traffic simulation

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