On parallel random number generation for accelerating simulations of communication systems

Brugger, Christian; Weithoffer, Stefan; Schryver, Christian De; Wasenmüller, U.; Wehn, Norbert

doi:10.5194/ars-12-75-2014

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…For the results presented in this paper, the library's Mersenne Twister implementation was used, because it is the same PRNG used by NetLogo, and its very large period of 2 19937 − 1 makes sub-stream overlapping highly unlikely to occur [5,53].…”

Section: Random Number Generators and Reproducible Simulationsmentioning

confidence: 99%

Parallelization Strategies for Spatial Agent-Based Models

Fachada

Lopes

Martins

et al. 2016

Int J Parallel Prog

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Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. Large scale emergent behavior in ABMs is population sensitive. As such, the number of agents in a simulation should be able to reflect the reality of the system being modeled, which can be in the order of millions or billions of individuals in certain domains. A natural solution to reach acceptable scalability in commodity multi-core processors consists of decomposing models such that each component can be independently processed by a different thread in a concurrent manner. In this paper we present a multithreaded Java implementation of the PPHPC ABM, with two goals in mind: 1) compare the performance of this implementation with an existing NetLogo implementation; and, 2) study how different parallelization strategies impact simulation performance on a shared memory architecture. Results show that: 1) model parallelization can yield considerable performance gains; 2) distinct parallelization strategies offer specific trade-offs in terms of performance and simulation reproducibility; and, 3) PPHPC is a valid reference model for comparing distinct implementations or parallelization strategies, from both performance and statistical accuracy perspectives.

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Section: Random Number Generators and Reproducible Simulationsmentioning

confidence: 99%

Parallelization Strategies for Spatial Agent-Based Models

Fachada

Lopes

Martins

et al. 2016

Int J Parallel Prog

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“…They also (a) do not provide a RNG mechanism for the parallel simulations and (b) rely on special add-on tools in programming languages, ignoring the readily available array job mechanism found in HPC systems for launching parallel tasks. As mentioned earlier, studies 11–15 show that advanced RNGs with special seeding techniques need to be deployed in order to provide adequate random numbers for the parallel simulations.…”

Section: Related Workmentioning

confidence: 99%

“…The quality of M&S strongly depends on the quality of employed random numbers. Studies 11–15 show that traditional RNGs are not adequate for parallel simulations; advanced RNGs with special seeding techniques need to be deployed to provide adequate random numbers for parallel simulations. Coddington 11 concludes that RNGs for parallel environments should (a) produce intra-processor streams of high quality in the usual statistical sense while exhibiting minimal inter-processor dependence, (b) be scalable, and (c) require no data movement between processors.…”

Section: Introductionmentioning

confidence: 99%

Scaling modeling and simulation on high-performance computing clusters

et al. 2019

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Large-scale modeling and simulation (M&S) applications that do not require run-time inter-process communications can exhibit scaling problems when migrated to high-performance computing (HPC) clusters if traditional software parallelization techniques, such as POSIX multi-threading and the message passing interface, are used. A comprehensive approach for scaling M&S applications on HPC clusters has been developed and is called “computation segmentation.” The computation segmentation is based on the built-in array job facility of job schedulers. If used correctly for appropriate applications, the array job approach provides significant benefits that are not obtainable using other methods. The parallelization illustrated in this paper becomes quite complex in its own right when applied to extremely large M&S tasks, particularly due to the need for nested loops. At the United States Food and Drug Administration, the approach has provided unsurpassed efficiency, flexibility, and scalability for work that can be performed using embarrassingly parallel algorithms.

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