Air pollution modelling using a Graphics Processing Unit with CUDA

Molnár, Ferenc; Szakaly, Tamas; Mészáros, Róbert; Lagzi, István

doi:10.1016/j.cpc.2009.09.008

Cited by 46 publications

(21 citation statements)

References 38 publications

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“…Usually, both ε and δ are chosen to fit some physical properties of the material under consideration. The molecular dynamics codes applied here can be found elsewhere [1][2][3][4][5][6][7][8]. We apply a simple cube box with periodic boundary conditions.…”

Section: Ii1 Molecular Dynamic Methodsmentioning

confidence: 99%

“…[4] the computing performance of MD simulation can be increased by 3.5 times. Later works shows the effectiveness of GPU computing reached 10-20 [5], 19 [6] or 24 times [7], even up to 100 times [8]. Recently, as noted in [7], with fast development of GPU technology there are many new possibilities in application of GPU computing in the field of scientific research, especially for MD simulations.…”

Section: Introductionmentioning

confidence: 99%

“…However, it consumes much computing time that limits the ability to treat a large scale simulation [3][4][5][6][7][8]. For example, to construct a MD model SiO 2 LiO 2 of the 3000 atoms on the PC takes about 60 hours.…”

Section: Introductionmentioning

confidence: 99%

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Accelerated MD Program Using CUDA Technology

Hue¹,

Hà²,

Hung³

2011

Comm. in Phys.

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Abstract. Molecular dynamic (MD) simulation is proven to be an important tool to study the structure as well as the physical properties at atomic level in materials science. However, it requires a huge computing time and hence limits the ability to treat a large scale simulation. In this paper we present a solution to speed up the MD simulation using CUDA technology (Compute Unified Device Architecture). We used the GeForce GTS 250 card with Version 2.30. The simulation is implemented for Lennard-Jones systems with periodic boundary conditions which consist of 1024, 2048, 4096 and 8192 atoms. The calculation shows that the computing time depends on the size of system and could be decreased by 37 times. This result indicates a possibility of constructing a large MD model with up to 10 5 atoms on the usual PC.

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Section: Ii1 Molecular Dynamic Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accelerated MD Program Using CUDA Technology

Hue¹,

Hà²,

Hung³

2011

Comm. in Phys.

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“…Any number of trajectories can be computed independently in parallel, and communication is required only to distribute tasks and gather results. Such a computation is called an embarrassingly parallel problem, and fits well to any parallel computing architecture, including grids [122,123], clusters of any size [124,125], and GPUs [107][108][109]. Most commonly, implementations follow the master-slave paradigm.…”

Section: Parallelization Of Atmospheric Dispersion Modelsmentioning

confidence: 99%

Dispersion modeling of air pollutants in the atmosphere: a review

et al. 2014

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Modeling of dispersion of air pollutants in the atmosphere is one of the most important and challenging scientific problems. There are several natural and anthropogenic events where passive or chemically active compounds are emitted into the atmosphere. The effect of these chemical species can have serious impacts on our environment and human health. Modeling the dispersion of air pollutants can predict this effect. Therefore, development of various model strategies is a key element for the governmental and scientific communities. We provide here a brief review on the mathematical modeling of the dispersion of air pollutants in the atmosphere. We discuss the advantages and drawbacks of several model tools and strategies, namely Gaussian, Lagrangian, Eulerian and CFD models. We especially focus on several recent advances in this multidisciplinary research field, like parallel computing using graphical processing units, or adaptive mesh refinement.

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“…It contains the schema CUDA instruction set and internal computation engine. GPU is characterized by processing parallel computation and dense data, so CUDA suites large-scale parallel computation field very well [12].…”

Section: Introductionmentioning

confidence: 99%