It is well known that Strassen and Winograd algorithms can reduce the computational<br />
costs associated with dense matrix multiplication. We have already shown<br />
that they are also very effective for software-based multiple precision floating-point<br />
arithmetic environments such as the MPFR/GMP library. In this paper, we show<br />
that we can obtain the same effectiveness for double-double (DD) and quadrupledouble<br />
(QD) environments supported by the QD library, and that parallelization<br />
can increase the speed of these multiple precision matrix multiplications. Finally, we<br />
demonstrate that our implemented parallelized Strassen and Winograd algorithms<br />
can increase the speed of parallelized LU decomposition.