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6iteration procedure and numerical discretization of the equations. A popular SIMPLE method [1] together with
finite-volume discretization of the equations on the basis of the home software package LOGOS [2] is used as a
numerical method. A problem of perturbations propagation from a harmonic-oscillations source in a fluid is
described for the assessment [3]. Space and time resolution necessary to provide acceptable accuracy of the
solution is estimated. The produced estimations are validated using the problem of propagation of harmonic
waves from a point source in a fluid.
References
1. Lashkin S.V., Kozelkov A.S., Yalozo A.V., Gerasimov V.Y., Zelensky D.K. Efficiency analysis of the parallel
implementation of the SIMPLE algorithm on multiprocessor computers // Journal Of Applied Mechanics And Technical
Physics, 2017, v.58, Issue 7, p. 1242-1259..
2. Kozelkov A.S., Kurulin V.V., Lashkin S.V., Shagaliev R.M., Yalozo A.V., Investigation of supercomputer capabilities
for the scalable numerical simulation of computational fluid dynamics problems in industrial applications //
Computational mathematics and mathematical physics, 2016, V. 56, Issue 8, P. 1524�1535.
3. Fenton J. D. A Fifth-Order Stokes Theory for Steady Waves // Coastal and Ocean Eng., 1985, v.111, Issue 2, p. 216-
234 [4]Zwart P.J., Gerber A.G., Belamri T. A Two-phase flow model for predicting cavitation dynamics // Fifth International
Conference on Multiphase Flow. Yokohama, Japan. 2004.
Algorithms and implementation of active knowledge in LuNA system
V. Malyshkin, V. Perepelkin
Institute of Computational Mathematics and Mathematical Geophysics SB RAS
Email: malysh@ssd.sscc.ru
DOI 10.24412/cl-35065-2021-1-01-77
Active knowledge development problems in the domain of numerical simulations on supercomputers is
discussed. Basic components of the LuNA system, which supports active knowledge, are concerned. Peculiari-
ties and limitations of the system, as well as its current condition and abilities are discussed. Applications of
LuNA for a series of tests are analyzed.
References
1. Valkovsky, V., Malyshkin, V.: Synthesis of parallel programs and systems on the basis of computational models.
Nauka, Novosibirsk, 1988 (in Russian).
2. Victor Malyshkin. Active Knowledge, LuNA and Literacy for Oncoming Centuries // Springer, LNCS, V. 9465 (2015),
pp. 292-303. DOI: 10.1007/978-3-319-25527-9_19.
3. Victor Malyshkin, Vladislav Perepelkin, and Georgy Schukin. Distributed Algorithm of Data Allocation in the
Fragmented Programming System LuNA // Springer, LNCS, V. 9251 (2015), pp. 80-85. DOI: 10.1007/978-3-319-21909-7_8.
Execution trace based optimization of fragmented programs performance
V. Perepelkin, V. Malyshkin
Institute of Computational Mathematics and Mathematical Geophysics SB RAS
Email: malysh@ssd.sscc.ru
DOI 10.24412/cl-35065-2021-1-01-78
Profiling and tracing of parallel programs execution is a source of information on quantitative characteris-
tics of efficiency, such as computing nodes load over time, communication subsystem load, memory consump-
tion, etc. It is essential, that the characteristics can be measured with connection to source code locations of
the program executed. This information can often be used to significantly improve performance of parallel
programs. In particular it is important for systems and tools for automatic parallel programs construction,
since a program can be reconstructed according to profiling and trace information automatically. In the work
we propose facilities for LuNA system, capable of optimizing fragmented programs efficiency based on trace
information. Experimental results are presented.
Research on the numerical method and grid parameters as they influence the simulation accuracy
for the floating bodies
K. S. Plygunova, V. V. Kurulin, D. A. Utkin
FSUE �Russian Federal Nuclear Center � All-Russia Research Institute of Experimental Physics� Nizhny Novgorod
Region, Sarov
Email: xenia28_94@mail.ru
DOI 10.24412/cl-35065-2021-1-01-79
The work studies the grid parameters, the time step size, the order of approximation by space and time as
they influence the accuracy of the problem solution with damped free vibrations of the cylinder on the water
surface [1, 2]. The numerical simulation method of the floating bodies is based on the solution of a system of
Navier-Stokes equations together with VOF method [3, 4]. The motion of the body is accounted for by the de-
formation of the computational grid [5]. CFS model is used to account for the surface tension forces [6]. The
method is realized on the basis of the home LOGOS software package [7].
References
1. Maskell S. J., Ursell F. The transient motion of a floating body // J. Fluid Mech., 1970. N. 44. P. 303-313.
2. Soichi Ito. Study of the transient heave oscillation of a floating cylinder // Massachusetts institute of technology.
1977.
3. Hirt C.W., Nichols B.D. Volume of fluid (VOF) method for the dynamics of free boundaries // J. Comput. Phys. 1981.
V. 39. P. 201-225.
4. Kozelkov A.S., Meleshkina D.P., Kurkin A.A., Tarasova N.V., Lashkin S.V., Kurulin V.V. Completely implicit method to
solve Navier-Stokes equations to compute multiphase flows with free surface // Computational technologies. 2016. Vol.
21. � 5. pp. 54-76.
5. Edward Luke, Eric Collins, Eric Blades, A fast mesh deformation method using explicit interpolation // Journal of
Computational Physics. 2012. N. 231. P. 586�601.
6. Brackbill J.U., Kothe D.B., Zemach C. A continuum method for modeling surface tension // J. Comput. Phys. 1992.
N.100. P. 335-354.
7. Kozelkov A.S., Kurulin V.V., Lashkin S.V., Shagaliev R.M., Yalozo A.V., Investigation of supercomputer capabilities
for the scalable numerical simulation of computational fluid dynamics problems in industrial applications //
Computational mathematics and mathematical physics. 2016. V. 56. N. 8. P. 1524�1535.
Optimizations of computations on manycore processors and accelerators for elastic waves simulation
A. F. Sapetina
Institute of Computational Mathematics and Mathematical Geophysics SB RAS
Email: afsapetina@gmail.com
DOI 10.24412/cl-35065-2021-1-01-80
The solution of compute-intensive problems of mathematical modeling requires the development of par-
allel programs. The choice of various mathematical methods, algorithms and computational architectures for
solving such problems, as well as the development of high-performance codes is a complex task. In solving it,
the researcher can be helped by the developed system of intellectual support based on the ontological ap-
