Accelerating Strassen-Winograd's matrix multiplication algorithm on GPUs

In this paper, we report on the development of an efficient GPU implementation of the Strassen-Winograd matrix multiplication algorithm for matrices of arbitrary sizes. We utilize multi-kernel streaming to exploit concurrency across sub-matrix operations in addition to intra-operation parallelism. We evaluate the performance of the implementation in comparison with CUBLAS-5.0 on Fermi and Kepler GPUs. The experimental results demonstrate the usefulness of Strassen's algorithm for practically relevant matrix sizes on GPUs, with up to 1.27X speedup for single-precision and 1.42X speedup for double-precision floating point computation.