Mixed parallel implementations of the top level step of Strassen and Winograd matrix multiplication algorithms

This paper presents parallel implementations of the top level of Strassen and Winograd algorithms for matrix multiplication that use mixed-parallelism, i.e., simultaneous exploitation of data- and task-parallelism. This paradigm allows a better task placement and reduces the communication costs. A comparison with the ScaLAPACK implementation of the matrix multiplication is given. We present a theoretical evaluation of the algorithms which is corroborated by experiments.