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- W4367156711 abstract "The problem of increasing the speed of big data analytics applications remains relevant. The solution may be to create new algorithms, or to increase the efficiency of existing implementations. Many studies are being conducted to optimize the operation of well-known algorithms in application to the computing complex as a whole, including the communications subsystem. At the same time, there is a need for efficient operation of the power of each node of the computing complex. In particular, the question of cache optimization and vectorization of a number of linear algebra algorithms that are part of the big data analytics software on parallel computing systems remains open. The purpose of this work is to increase the efficiency of the software implementation of a parallel linear algebra algorithm using the proposed optimization method. The proposed method was applied to the Jacobi method of singular decomposition of dense matrices. Computational experiments were carried out to determine the speedup of the proposed modification in relation to other software implementations. The results of computational experiments have shown the effectiveness of the proposed method" @default.
- W4367156711 created "2023-04-28" @default.
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- W4367156711 date "2023-01-01" @default.
- W4367156711 modified "2023-09-25" @default.
- W4367156711 title "Efficient implementation of singular value decomposition on shared memory computing systems" @default.
- W4367156711 doi "https://doi.org/10.34219/2078-8320-2023-14-1-112-119" @default.
- W4367156711 hasPublicationYear "2023" @default.
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