It is well known that Strassen and Winograd algorithms can reduce the computational
costs associated with dense matrix multiplication. We have already shown
that they are also very effective for software-based multiple precision floating-point
International Journal of Numerical Methods and Applications, Vol.7, Issue 2, 2012, Pages 107 - 119 2014年11月
We evaluate the performance of the Krylov subspace method by using highly
efficient multiple precision sparse matrix-vector multiplication (SpMV).
BNCpack is our multiple precision numerical computation library based on
MPFR/GMP, which is one of t...
The Strassen algorithm and Winograd's variant accelerate matrix
multiplication by using fewer arithmetic operations than standard matrix
multiplication. Although many papers have been published to accelerate single-
as well as double-precision mat...
International Journal of Numerical Methods and Applications, Volume 9, Number 2, 2013, pp.85-108 2013年6月
We propose a practical implementation of high-order fully implicit
Runge-Kutta(IRK) methods in a multiple precision floating-point environment.
Although implementations based on IRK methods in an IEEE754 double precision
environment have been repo...