Hierarchical matrix-matrix multiplication based on multiprocessor tasks

We consider the realization of matrix-matrix multiplication and propose a hierarchical algorithm implemented in a task-parallel way using multiprocessor tasks on distributed memory. The algorithm has been designed to minimize the communication overhead while showing large locality of memory references. The task-parallel realization makes the algorithm especially suited for cluster of SMPs since tasks can then be mapped to the different cluster nodes in order to efficiently exploit the cluster architecture. Experiments on current cluster machines show that the resulting execution times are competitive with state-of-the-art methods like PDGEMM.