Parallelization of tau-leap coarse-grained Monte Carlo Simulations on GPUs

Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010

Volumn , Issue , 2010, Pages

  Xu, Lifan ^a   Taufer, Michela ^a   Collins, Stuart ^a   Vlachos, Dionisios G ^a  

^a Institute University of Delaware Newark   (United States)

Author keywords

Data parallelism on GPUs; GPU programming; Monte Carlo methods; Parallel programming

Indexed keywords

COARSE-GRAINED; COARSE-GRAINED MONTE CARLO SIMULATIONS; DATA PARALLELISM; DEGREE OF PARALLELISM; GPU PROGRAMMING; ITS DATA; MASTER-SLAVE; MONTE CARLO; MULTISCALES; PARALLEL SIMULATIONS; PARALLELIZATIONS; PERFORMANCE EVALUATION; PERFORMANCE IMPROVEMENTS; SIMULATION FRAMEWORK; SIMULATION PROFILES; SPATIALLY-DISTRIBUTED SYSTEM;

CELL MEMBRANES; COMPUTER SIMULATION; COMPUTER SOFTWARE SELECTION AND EVALUATION; CYTOLOGY; DATA STRUCTURES; DISTRIBUTED PARAMETER NETWORKS; GRAIN BOUNDARIES; PARALLEL PROGRAMMING; PHASE TRANSITIONS; PROGRAM PROCESSORS; SINGLE CRYSTALS;

MONTE CARLO METHODS;

EID: 77953976369     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2010.5470402     Document Type: Conference Paper

References (12)

1. Y. Cao, D. Gillespie, and L. Petzold. Avoiding Negative Populations in Explicit Poisson Tau-leaping. Journal of Chemical Physics, 123(5):541041- 0541048, 2005.
2. A. Chatterjee, D. Vlachos, and M. Katsoulakis. Binomial Distribution based Tau-leap Accelerated Stochastic Simulation. Journal of Chemical Physics, 122:0241121-0241126, 2005.
3. A. Chatterjee and D. G. Vlachos. Temporal Acceleration of Spatially Distributed Kinetic Monte Carlo Simulations. Journal of Computational Physics, 211:596615, 2006.
4. A. Chatterjee and D. G. Vlachos. An Overview of Spatial Microscopic and Accelerated Kinetic Monte Carlo Methods. J. of Computer-aided Materials Design, 14(2):253-308, 2007.
5. S. D. Collins, A. Chatterjee, and D. G. Vlachos. Coarsegrained Kinetic Monte Carlo Models: Complex Lattices, Multicomponent Aystems, and Homogenization at the Stochastic Level. J. Chem. Phys., 129(18), 2008.
6. D. Gillespie. Approximate Accelerated Stochastic Simulation of Chemically Reacting Systems. Journal of Chemical Physics, 115(4):1716-1733, 2001.
7. E. Martinez, J. Marian, M. H. Kalos, and J. M. Perlado. Synchronous Parallel Kinetic Monte Carlo for Continuum Diffusion-reaction Systems. Journal of Computational Physics, 227:3804-3823, Apr. 2008.
8. NVIDIA. NVIDIA CUDA Programming Guide Version 2.2. 2009.
9. T. Preis, P. Virnau, W. Paul, and J. J. Schneider. GPU Accelerated Monte Carlo Simulation of the 2D and 3D Ising Model. J. of Computational Phys., 228(12):4468-4477, 2009.
10. T.T. Marquez-Lago and K. Burrage. Binomial Tau-leap Spatial Stochastic Simulation Algorithm for Applications in Chemical Kinetics. J. of Chem. Phys., 127(10), 2007.
11. J.A. van Meel, A. Arnold, D. Frenkel, S.F. Portegies-Zwart, and R.G. Belleman. Harvesting Graphics Power for MD Simulations. Molecular Simulation, 2008.
12. K. Yasuda. Accelerating Density Functional Calculations with Graphics Processing Unit. J. of Chem. Theory Comput., 4(8), 1230-1236 2008.