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Politechniki Koszalińskiej

Widok standardowy Widok MARC Widok ISBD

Algorithm-based fault tolerance in linear algebra tasks / Oleg Masliennikov.

Autor: Rodzaj materiału: ArtykułJęzyk: angielski Praca zawiera:
  • International Conference on Signals and Electronic Systems ICSES 2002 (2002 ; Wrocław / Świeradów Zdrój, Polska). ICSES'2002
Tematy: Rodzaj/forma: W: ICSES'2002 : proceedings of the International Conference on Signals and Electronic Systems, Wrocław - Świeradów Zdrój, Poland 24-27 September 2002 / [ed. by R. Makowski]. - Wrocław : Wrocławskie Towarzystwo Naukowe, 2002. - s. 313-318Streszczenie: The modification of weighted checksum method is proposed, which allows to derive the fault tolerant versions of most linear algebra algorithms. The purpose is detection and correction of calculation errors occurred due to transient hardware faults. Usine the proposed method, the fault-tolerant version of Faddeeva algorithm is designed in this paper. The computational complexity of new algorithm is increased approximately on O(N2~)) multiply-add operations in comparison with the original one. However, new algorithm enables to detect and to correct a single error in an arbitrary row or column of input data matrices at the each algorithm step. Hence, it is possible to correct up to N2~) and (N2~)/2+N P) single errors during realization of whole Jordan-Gauss and Faddeeva algorithms respectively. Finally, the results of experimental verification of the proposed algorithm are represented.
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The modification of weighted checksum method is proposed, which allows to derive the fault tolerant versions of most linear algebra algorithms. The purpose is detection and correction of calculation errors occurred due to transient hardware faults. Usine the proposed method, the fault-tolerant version of Faddeeva algorithm is designed in this paper. The computational complexity of new algorithm is increased approximately on O(N2~)) multiply-add operations in comparison with the original one. However, new algorithm enables to detect and to correct a single error in an arbitrary row or column of input data matrices at the each algorithm step. Hence, it is possible to correct up to N2~) and (N2~)/2+N P) single errors during realization of whole Jordan-Gauss and Faddeeva algorithms respectively. Finally, the results of experimental verification of the proposed algorithm are represented.

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