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Specifics of using supercomputing technologies for solving multidimensional linear inverse problems for integral equations

The meeting of the seminar

«Supercomputer technologies

in science, education and industry»

Chairman of the organizing committee
Sadovnichy V.A. Sadovnichy V.A.
academician, rector of Lomonosov Moscow State University

Co-Chairs of the organizing committee
Sokolov I.A. Sokolov I.A.
academician, dean of the faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University
Avraamova O.D. Avraamova O.D.
Doctor of Technical Sciences, Acting Director of the RCC MSU
Tikhonravov A.V. Tikhonravov A.V.
Doctor of Physical and Mathematical Sciences, Professor, RCC MSU
Stepanenko V.M. Stepanenko V.M.
Doctor of Physical and Mathematical Sciences, Deputy Director of the RCC MSU

Scientific Secretary of the seminar
  Mikhail Shubin
Research Computing Center of Moscow State University

 

The seminar was organized on the basis of the Scientific and Educational Center «Supercomputer Technologies». The reports cover all aspects of the use of supercomputers, parallel computing systems and distributed data processing methods for solving large computational problems. The seminar has a pronounced interdisciplinary nature, however, the various nuances of using supercomputer technologies are of interest to researchers from various fields.

The seminar is supported by the Moscow Center of Fundamental and Applied Mathematics.

Researchers, teachers, graduate and undergraduate students are invited.

 


PROGRAM
16:20 (GMT+3)
Dmitry Vitalyevich Lukyanenko Doctor of Physical and Mathematical Sciences, Professor in the Department of Mathematics, Division of Applied Mathematics, Faculty of Physics, MSU

Specifics of using supercomputing technologies for solving multidimensional linear inverse problems for integral equations

This presentation examines the application of supercomputing technologies to solving multidimensional linear inverse problems involving integral equations. In practice, such inverse problems are typically ill-posed; upon discretization, they reduce to the need to solve large-scale, ill-conditioned, overdetermined systems of linear algebraic equations with dense matrices. Two key issues are addressed: the adaptation of parallel algorithms to supercomputer architectures, and the management of machine rounding errors — which accumulate during large-scale computations and can critically affect the quality of the regularized solution.


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