Инженерные технологии и системы (Jun 2019)

The Multiplication Method with Scaling the Result for High-Precision Residue Positional Interval Logarithmic Computations

  • Anastasia S. Korzhavina,
  • Vladimir S. Knyazkov

DOI
https://doi.org/10.15507/2658-4123.029.201902.187-204
Journal volume & issue
Vol. 29, no. 2
pp. 187 – 204

Abstract

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Introduction. The solution of the simulation problems critical to rounding errors, including the problems of computational mathematics, mathematical physics, optimal control, biochemistry, quantum mechanics, mathematical programming and cryptography, requires the accuracy from 100 to 1 000 decimal digits and more. The main lack of high-precision software libraries is a significant decrease of the speed-in-action, unacceptable for practical problems, in particular, when performing multiplication. A way to increase computation performance over very long numbers is using the residue number system. In this work, we discuss a new fast multiplication method with scaling the result using original hybrid residue positional interval logarithmic floating-point number representation. Materials and Methods. The new way of the organizing numerical information is a residue positional interval logarithmic number representation in which the mantissa is presented in the residue number system, and information on an absolute value (the characteristic) in the interval logarithmic number system that makes it possible to accelerate performance of comparison and scaling is developed to increase the speed of calculations; to compare modular numbers, the provisions of interval analysis are used; to scale modular numbers, the properties of the logarithmic number system and approximate interval calculations using the Chinese reminder theorem are used. Results. A new fast multiplication method of floating-point residue-represented numbers is developed and patented; the authors evaluated the developed method speed-in action, compared the developed method with classical and pipelined multiplication methods of long numbers. Discussion and Conclusion. The developed method is 2.4–4.0 times faster than the pipelined multiplication method, and is 6.4–12.9 times faster than classical multiplication methods.

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