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Calculating the Moore–Penrose Generalized Inverse on Massively Parallel Systems

Описание

Тип публикации: статья из журнала

Год издания: 2022

Идентификатор DOI: 10.3390/a15100348

Ключевые слова: cuda, generalized cholesky factorization, gpu, moore–penrose generalized inverse, strassen’s matrix inversion

Аннотация: In this work, we consider the problem of calculating the generalized Moore–Penrose inverse, which is essential in many applications of graph theory. We propose an algorithm for the massively parallel systems based on the recursive algorithm for the generalized Moore–Penrose inverse, the generalized Cholesky factorization, and StrasПоказать полностьюsen’s matrix inversion algorithm. Computational experiments with our new algorithm based on a parallel computing architecture known as the Compute Unified Device Architecture (CUDA) on a graphic processing unit (GPU) show the significant advantages of using GPU for large matrices (with millions of elements) in comparison with the CPU implementation from the OpenCV library (Intel, Santa Clara, CA, USA). © 2022 by the authors.

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Издание

Журнал: Algorithms

Выпуск журнала: Vol. 15, Is. 10

Номера страниц: 348

ISSN журнала: 19994893

Издатель: MDPI

Персоны

  • Stanojević V. (Faculty of Sciences and Mathematics, University of Niš, Niš, 18000, Serbia)
  • Kazakovtsev L. (Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation, Institute of Informatics and Telecomunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, 660014, Russian Federation)
  • Stanimirović P.S. (Faculty of Sciences and Mathematics, University of Niš, Niš, 18000, Serbia, Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation)
  • Rezova N. (Institute of Informatics and Telecomunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, 660014, Russian Federation)
  • Shkaberina G. (Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation, Institute of Informatics and Telecomunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, 660014, Russian Federation)

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