Abstract: The object of research is the process of choosing the optimal hardware architecture for organizing resource-intensive computing. The subject of the research is the process of solving optimization problems by genetic algorithms on GPU and CPU architectures. The influence of the choice of hardware architecture on the process of solving the optimization problem is shown: the absolute and relative dependences of the slowdown of the computing process, when choosing an irrational hardware architecture, on the number of individuals processed by the algorithm are determined. It is established that for the considered problem, the boundary of the most efficient hardware configuration can be in the range from 1000 to 5000 individuals. For this reason, it is advisable to describe the blurring of the boundary of an effective hardware configuration as a set of pairs “number of individuals — membership in a transition”. The research method is based on an analysis of the results of a computational experiment. The purpose of the experiment is to determine the dependencies of the runtime of the genetic algorithm on the GPU and CPU architectures on the number of individuals generated (chromosomes). The dependences of the minimum and maximum time of the genetic algorithm running on the GPU and CPU on the number of individuals are compared. It is shown that when solving the considered problem, the minimum and maximum time dependences of the algorithm performed on the GPU are close to a linear function; the minimum time dependences of the algorithm performed on CPU are close to a linear function, and the maximum to polynomial.

Keywords: preferred hardware architecture, intelligent system, graphics processing unit, central processing unit, adaptation, genetic algorithm, optimize calculations, evolution, multithreading, modeling

References:
Fan Zhang, Zheng Li, Bingnan Wang, Maosheng Xiang, Wen Hong. Hybrid general-purpose computation on GPU (GPGPU) and computer graphics synthetic aperture radar simulation for complex scenes// International Journal of Physical Sciences Vol. 7(8), pp. 1224-1234, 16 February, 2012
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Agibalov O.I., Ventsov N.N. Otsenka zavisimostey vremeni raboty geneticheskogo algoritma, vypolnyaemogo na CPU i GPU // Kibernetika i programmirovanie. — 2017.-¹ 6.-S.1-8. DOI: 10.25136/2306-4196.2017.6.24509. URL: http://e-notabene.ru/kp/article_24509.html
Glushan' V.M., Lavrik P.V. Raspredelennye SAPR. Arkhitektura i vozmozhnosti.-Staryy Oskol: TNT, 2014

Abstract: In this paper, the author considers the problem of automatic parallelization of C programs (mainly computational) with the use of Cilk ++ directives, with the help of a limited set of which parallelism in tasks can be clearly expressed. To solve this problem, the concept of recognizing object-event models, potentially capable of parsing and transforming arbitrary texts, is formulated. This concept is a development of the theory of object-event models proposed by the author earlier, which, in the marginal formulation, are equivalent to advanced Turing machines. A general approach of the theory of object-event models is used, which asserts the possibility of describing arbitrary algorithms using these models. The technology of analysis and transformation of both structured and non-structured texts with the use of recognizing object-event models is proposed. A strategy is proposed for automatic parallelization of C programs using Cilk ++ directives based on this technology. Using the example of automatic parallelization of a simple computing program, data on acceleration and efficiency of parallelization are obtained. It is argued that the developed technology can be used as part of a program generating system for parallelizing the generated programs.

Keywords: program generation system, algorithm transformation, algorithm recognition, object-event models, Cilk, C programming language, automatic parallelizing, logical programming, task parallelism, parallel computations

References:
Shapovalov O.V., Andreev A.E., Fomenkov S.A. Razrabotka metodov avtomatizatsii rasparallelivaniya programm dlya sistem s obshchey pamyat'yu // Izvestiya YuFU. Tekhnicheskie nauki. 2015. ¹3 (164). S.24-35.
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Taherkhani A. Automatic Algorithm Recognition Based on Programming Schemas and Beacons: A Supervised Machine Learning Classification Approach [Elektronnyy resurs] // Doctoral Dissertation. Aalto University, 2013. 254 pp. URL: http://lib.tkk.fi/Diss/2013/isbn9789526049908/isbn9789526049908.pdf (data obrashcheniya: 19.11.2018).
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