PERFORMANCE STUDY OF GRAPHIC PROCESSORS USAGE IN COMPUTATIONAL NANOTECHNOLOGY PROBLEMS
( Pp. 5-12)

More about authors
Popova Nina Nikolaevna kandidat fiziko-matematicheskih nauk; docent fakulteta vychislitelnoy matematiki i kibernetiki
Lomonosov Moscow State University Nikishin Nikolai G. aspirant, fakultet Vychislitelnoy matematiki i kibernetiki
the Lomonosov Moscow State University, Moscow
Abstract:
Computational nanotechnology is strongly connected with modern high-performance computing. With fast evolution of graphical processing units (GPU), general-purpose computing (GPGPU) became a popular choice for computationally demanding tasks. Tasks in nanomaterial researches with mass-spectrometers usage for analysis and material creation are good examples of such kind of tasks. Paper is devoted to ions behavior modeling in traps of mass-spectrometers based on Fourier transform. We use heterogeneous computational systems with GPU inside for calculation. Particle-in-cell model is used for direct modeling of ions behavior. We also use two GPU libraries: CULA and cuFFT. Paper shows, that some GPU-oriented libraries could significantly ease the development of parallel algorithms for GPU and allow to get good performance of parallel applications. Calculations were performed on several systems including “Lomonosov” supercomputer in Moscow State University. Paper shows, that different strategies of mapping parallel processes to nodes could significantly effects on performance because of parallel access to an every single GPU from multiple processes. Results obtained in this work could be useful for big molecular structure modeling, for solving computational nanotechnology problems on modern high-performance parallel computational systems with GPU
How to Cite:
Popova N.N., Nikishin N.G., (2014), PERFORMANCE STUDY OF GRAPHIC PROCESSORS USAGE IN COMPUTATIONAL NANOTECHNOLOGY PROBLEMS. Computational Nanotechnology, 2 => 5-12.
Reference list:
Wilt N. The CUDA Handbook: A Comprehensive Guide to GPU Programming. Addison-Wesle, 2013. P. 528
Parallel Computing: From Multicores and GPU s to Petascale / Chapman B., Desprez F., Gerhard R., et al. IOS Press, 2010. Vol. 19. P. 739
Popov A.M. Vychislitel nye nanotekhnologii: uchebnoe posobie. M.: KNORUS, 2014. 312 c.
KHokni R., Istvud Dzh. CHislennoe modelirovanie metodom chastits: per.s angl. M.: Mir, 1987. 640 s.
Nikolaev E. N. Heeren R. M. A. Popov A. M. Realistic modeling of ion cloud motion in a Fourier transform ion cyclotron resonance cell by use of a particle-in-cell approach // Rapid Communication in Mass Spectrometry. 2007. Vol. 21(22). P. 3527-3546.
Misharin A., Popov A. Parallel numerical code parTfield to simulate ion optical elements for any electrode geometry // Proc. 60th ASMS Conference on Mass Spectrometry and Allied Topics, Vancouver, Canada, May 20-24. 2012.
Samarskiy A. A., Nikolaev E.S. Metody resheniya setochnykh uravneniy. M.: Nauka, 1978. 590 s.
Malony A.D., et al. Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs // International Conference on Parallel Processing. ICPP 2011, Taipei, Taiwan, September 13-16. 2011. P. 176-185.
Keywords:
high performance computing, heterogeneous computing system, nanotechnology, mass spectroscopy, CUDA, The fast Fourier transform.


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