Abstract
Problems of improving the efficiency of the Monte Carlo numerical simulation of solar radiation propagation in the Earth’s atmosphere by transition from sequential to parallel computations are discussed. A new parallel algorithm oriented to a computational system with the NVIDIA CUDA enabled graphics processor is presented. The efficiency of parallelization is analyzed by an example of calculating the upward and downward fluxes of solar radiation both in a vertically homogeneous and in an inhomogeneous model of the atmosphere. The results of testing the new algorithm under various atmospheric conditions including continuous single-layered and multilayered cloudiness are presented, with allowance for selective molecular absorption and without regard to it. The results of testing the code using video cards with different computational capabilities are analyzed. It is shown that the changeover of computing from conventional PCs to the architecture of graphics processors gives more than a hundredfold increase in performance and fully reveals the capabilities of the technology used.
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O. Dubovik, T. Lapyonok, P. Litvinov, M. Herman, D.Fuertes, F. Ducos, A. Lopatin, A. Chaikovsky, B. Torres, Y. Derimian, X. Huang, M. Aspetsberger, and C. Federspiel, “GRASP: A versatile algorithm for characterizing the atmosphere,” SPIE: Newsroom (2014). doi 10.1117/2.1201408.005558
Radiative Transfer in Scattering and Absorbing Atmospheres: Standard Computational Procedures, Ed. by J. Lenoble (A Deepak Pub, 1986).
G. I. Marchuk, G. A. Mikhailov, M. A. Nazaraliev, R. A. Darbinyan, B. A. Kargin, and B. S. Elepov, Monte Carlo Methods in Atmospheric Optics (Nauka, Novosibirsk, 1976) [in Russian].
A. Marshak and A. B. Davis, 3D Radiative Transfer in Cloudy Atmospheres (Springer, Berlin, 2005).
G. Moore, “Litography and the future of Moore’s law,” Proc. SPIE—Int. Soc. Opt. Eng. 2437, 2–17 (1995).
D. R. Kirkby and D. T. Delpy, “Parallel operation of Monte Carlo simulations on a diverse network of computers,” Phys. Med. Biol. 42 (6), 1203–1208 (1997).
A. Colasanti, G. Guida, A. Kisslinger, R. Liuzzi, M. Quarto, P. Riccio, G. Roberti, and F. Villani, “Multiple processor version of a Monte Carlo code for photon transport in turbid media,” Comput. Phys. Commun. 132 (1–2), 84–93 (2000).
A. V. Kozhevnikova, M. V. Tarasenkov, and V. V. Belov, “Parallel computations for solving problem of the reconstruction of the reflection coefficient of the Earth’s surface by satellite data,” Atmos. Ocean. Opt. 26 (4), 326–328 (2013).
B. M. Glinskii, A. S. Rodionov, M. A. Marchenko, D. I. Podkorytov, and D. V. Vins, “Agent-oriented approach to distributed statistical simulation at exascale computers,” Vestn. Yuzhny Ural. Gos. Univ., No. 18 (277), Is. 12, 94–99 (2012).
K. N. Volkov, Yu. N. Deryugin, V. N. Emel’yanov, A. G. Karpenko, A. S. Kozelkov, and I. V. Teterina, Methods for Acceleration Gas Dynamic Computations on Non-structured Grids (Fizmatlit, Moscow, 2014) [in Russian].
A. V. Boreskov, A. A. Kharlamov, N. D. Markovskii, D. N. Mikushin, E. V. Mortikov, A. A. Myl’tsev, N. A. Sakharnykh, and V. A. Frolov, Parallel GPU Computations: Architecture and CUDA Program Model (Izd-vo Mosk. Un-ta, Moscow, 2012) [in Russian].
C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18 (5), 050902–1 (2013).
I. I. Fiks, “The use of graphics processors for Monte Carlo solution of the light propagation problem in fluorescent tomography,” Vestn. Nizhegorodsk. Un-ta im. N.I. Lobachevskogo, No. 4(1), 190–195 (2011).
M. Yu. Kirillin, I. I. Fiks, A. R. Katichev, A. V. Gorshkov, and V. P. Gergel’, “High-efficient computations for problems of biomedical optical diagnostics,” in Supercomputer Technologies in Science, Education, and Industry, Ed. by V.A. Sadovnichii, G.I. Savin, Vl.V. Voevodin, Is. 3 (Izd-vo Mosk. Un-ta, Moscow, 2012) [in Russian].
D. A. Petrov, “Simulation of optical methods in biomedical diagnostics,” Koncept 11, 2851–2855 (2016).
E. Alerstam, W. C. Y. Lo, T. D. Han, J. Rose, S. Anderson-Engels, and L. Lilge, “Next-generation acceleration and code optimization for light transport in turbid media using GPUs,” Biomed. Opt. Express 1 (2), 658–675 (2010).
E. Alerstam, T. Svensson, and S. Anderson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13 (6), 060504–1 (2008).
F. Cai and S. He, “Using graphics processing units to accelerate perturbation Monte Carlo simulation in a turbid medium,” J. Biomed. Opt. 17 (4), 040502–1 (2012).
P. Martinsen, J. Blaschke, R. Kunnenmeyer, and R. Jordan, “Accelerating Monte Carlo simulations with an NVIDIA graphics processor,” Comput. Phys. Commun. 180 (10), 1983–1989 (2009).
Q. Fang and D. A. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units,” Opt. Express 17 (22), 20178–20190 (2009).
N. Ren, J. Liang, X. Qu, J. Li, B. Lu, and J. Tian, “GPU-based Monte Carlo Simulation for light propagation in complex heterogeneous tissues,” Opt. Express 18 (7), 6811–6823 (2010).
D. S. Efremenko, D. G. Loyola, A. Doicu, and R. J. D. Spurr, “Multi-core-CPU and GPU-accelerated radiative transfer models based on the discrete ordinate method,” Comput. Phys. Commun. 185 (12), 3079–3089 (2014).
D. Ramon, F. Steinmetz, M. Compiegne, and D. Jolivet, Massively parallel Monte-Carlo radiative transfer code on a desktop PC. http://www-loa.univ-lille1.fr/workshops/Trattoria-2015/documents/posters/Didier_Ramon. pdf (last access: 1.10.2017).
S. A. Clough, M. J. Iacono, and J. L. Moncet, “Line-byline calculations of atmospheric fluxes and cooling rates: Application to water vapor,” J. Geophys. Res., D 97, 16519–16535 (1992).
www.nvidia.ru (lass access: 01.10.2017).
T. V. Russkova and T. B. Zhuravleva, “Optimization of sequential code for simulation of solar radiation transfer in a vertically heterogeneous environment,” Atmos. Ocean. Opt. 30 (2), 169–175 (2017).
A. D. Frank-Kamenetskii, Simulation of Neutron Trajectories in Monte Carlo Computations of Reactors (Atomizdat, Moscow, 1978) [in Russian].
Dzh. Sanders and E. Kendrot, CUDA Technology in Examples: Introduction in Programming of Graphics Processors (DMK Press, Moscow, 2015) [in Russian].
M. E. Zhukovskii and R. V. Uskov, “Simulation of radiative emission of electrons using hybrid supercomputers,” Vychisl. Metody Programmir. 13 (1), 271–279 (2012).
W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in Fortran 77: The Art of Scientific Computing. Volume 1 of Fortran Numerical Recipes (University Press, Cambridge, 1986).
G. Marsaglia, “Random number generators,” J. Mod. Appl. Stat. Methods 2 (1), 2–13 (2003).
A. Lee, C. Yau, M. B. Giles, A. Doucet, and C. C. Holmes, “On the utility of graphic cards to perform massively parallel simulation of advanced Monte Carlo methods,” J. Comput. Graph. Stat. 19 (4), 769–789 (2010).
G. Marsaglia, Diehard battery of tests of randomness. The Marsaglia random number (Florida State University, Department of Statistics, 1995).
G. L. Miller, “Riemann’s hypothesis and tests for primarily,” J. Comput. Syst. Sci. 13, 300–317 (1976).
M. O. Rabin, “Probabilistic algorithm for testing primarily,” J. Number Theory 20 (1), 128–138 (1980).
M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator,” ACM Trans. Model. Comput. Sim. 8 (1), 3–30 (1998).
M. Matsumoto and T. Nishimura, “Dynamic creation of pseudorandom number generators,” in Monte Carlo and Quasi-Monte Carlo Methods (Springer, 2000), p. 56–69.
M. Hess, P. Koepke, and I. Schult, “Optical properties of aerosols and clouds: The software package OPAC,” Bull. Am. Meteorol. Soc. 79 (5), 831–844 (1998).
V. S. Komarov and N. Ya. Lomakina, Statistical Models of the Boundary Air Layer of Western Siberia (Publishing House IAO SB RAS, Tomsk, 2008) [in Russian].
I. P. Mazin, A. Kh. Khrgian, and I. M. Imyaninov, Clouds and Cloudy Atmosphere (Gidrometizdat, Leningrad, 1989) [in Russian].
G. P. Anderson, S. A. Clough, F. X. Kneizys, J. H. Chetwynd, and E. P. Shettle, Atmospheric Constituent Profiles (0–120 km) (Air Force Geophysics Laboratory, 1986).
K. M. Firsov and A. B. Smirnov, “Representation of transmission functions by exponential series,” Atmos. Ocean. Opt. 8 (8), 659–661 (1995).
S. D. Tvorogov, “Some aspects of the problem of representation of the absorption function by a series of exponents,” Atmos. Ocean. Opt. 7 (3), 165–172 (1994).
J. Fischer and H. Grassl, “Detection of cloud top height from backscattered radiances within the oxygen A band. Part 1. Theoretical study,” J. Appl. Meteorol., No. 30, 1245–1259 (1991).
R. B. A. Koelemeijer, P. Stammes, J. W. Hovenier, and J. F. de Haan, “A fast method for retrieval of cloud parameters using oxygen a band measurements from the global ozone monitoring experiment,” J. Geophys. Res. 106, 3475–3490 (2001).
V. V. Badaev, M. S. Malkevich, B. Nizik, and G. Tsimmerman, “Estimation of optical parameters of the Earth’s surface, ocean, and atmosphere from INTERKOSMOC 20 and 21 satellites,” Issled. Zemli Kosmosa, No. 5, 18–29 (1985).
Yu. M. Timofeyev, A. V. Vasilyev, and V. V. Rozanov, “Information content of the spectral measurement of the 0.76 μm O2 outgoing radiation with respect to the vertical aerosol optical properties,” Adv. Space Res. 16 (10), 91–94 (1995).
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Original Russian Text © T.V. Russkova, 2017, published in Optika Atmosfery i Okeana.
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Russkova, T.V. Monte Carlo Simulation of the Solar Radiation Transfer in a Cloudy Atmosphere with the Use of Graphic Processor and NVIDIA CUDA Technology. Atmos Ocean Opt 31, 119–130 (2018). https://doi.org/10.1134/S1024856018020100
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DOI: https://doi.org/10.1134/S1024856018020100