Efficient biased random bit generation for parallel lattice gas simulations |
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| Affiliation: | 1. Lawrence Livermore National Laboratory, PO Box 808, L-419, Livermore, CA 94550, USA;2. Department of Applied Science, University of California, Davis, P.O. Box 808, L-794, Livermore, CA 94550, USA;1. R&D Education Center for Fuel Cell Materials & Systems, Jeonju 561-756, Republic of Korea;2. Department of Energy Storage and Conversion Engineering, Jeonju 561-756, Republic of Korea;3. School of Chemical Engineering, Chonbuk National University, Jeonju 561-756, Republic of Korea;4. Buan Fuel Cell Center, Korea Institute of Energy Research (KIER), Jellabuk-do 56332, Republic of Korea;5. School of Chemical Engineering, Chonnam National University, Gwang-ju 61186, Republic of Korea;6. Department of Chemistry, Indian Institute of Science Education and Research (IISER), Tirupati, 517507, India;1. State Key Laboratory for Oxo Synthesis and Selective Oxidation, Suzhou Research Institute of LICP, Lanzhou Institute of Chemical Physics (LICP), Chinese Academy of Sciences, Lanzhou 730000, China;2. Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China;1. State Key Laboratory of Clean Energy Utilization, Institute of Thermal Power Engineering of Zhejiang University, Hangzhou 310027, China;2. South China Institute of Environmental Sciences. Ministry of Ecology and Environment, Guangzhou 510535, China;1. Institute of Molecular Science, Innovation Center of Chemistry and Molecular Science, Shanxi University, Taiyuan 030006, PR China;2. Key Laboratory of Materials for Energy Conversion and Storage of Shanxi Province, Taiyuan 030006, PR China;3. Institute of Materials Physical Chemistry, Huaqiao University, Quanzhou 362021, PR China;4. Department of Chemical Engineering, Tatung University, Taipei City 104, Taiwan;1. School of Materials Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, PR China;2. State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing, 100029, PR China;3. Beijing Key Laboratory of Energy Environmental Catalysis, Beijing University of Chemical Technology, Beijing, 100029, PR China;4. Integrated Composites Lab (ICL), Department of Chemical & Biomolecular Engineering, University of Tennessee, Knoxville, TN, 37996, USA;1. Departamento de Química Inorgánica, Facultad de Ciencias, Universidad de Córdoba, Campus de Rabanales, Edificio Marie Curie, 14071 Córdoba, Spain;2. Departamento de Ingeniería Química, Facultad de Ciencias, Universidad de Málaga, Campus de Teatinos, 29071 Málaga, Spain |
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| Abstract: | Lattice gas methods are often used to model the kinetics of a variety of diffusive systems. One of the main advantages of these methods is the ease at which they can be parallelized using simple bit vector operations. However, to describe the kinetics of the lattice gas in a randomly biased fashion, it is necessary to efficiently generate a randomly biased bit vector. If one generates a random floating point number per bit, then this is very costly. In this paper, an efficient algorithm is developed that leads to a fully bit vector implementation of a lattice gas automation while significantly reducing the amount of needed generated random floating point numbers. The bit vector algorithm using the new random biased bit vector algorithm is tested on a lattice gas method whose solution is modeled by the solution of the 1-D Burgers' equation. This new lattice gas method is then implemented on the BBN TC2200 and CRAY 90 parallel processors. |
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