Combining building blocks for parallel multi-level matrix multiplication
Parallel Computing
Adaptive approaches for efficient parallel algorithms on cluster-based systems
International Journal of Grid and Utility Computing
A Divide-and-Conquer Strategy and PVM Computation Environment for the Matrix Multiplication
ICA3PP '09 Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing
Graph expansion and communication costs of fast matrix multiplication: regular submission
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Work-stealing for mixed-mode parallelism by deterministic team-building
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Communication-optimal parallel algorithm for strassen's matrix multiplication
Proceedings of the twenty-fourth annual ACM symposium on Parallelism in algorithms and architectures
Graph expansion and communication costs of fast matrix multiplication
Journal of the ACM (JACM)
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In this paper we study the impact of the simultaneous exploitation of data- and task-parallelism, so called mixed-parallelism, on the Strassen and Winograd matrix multiplication algorithms. This work takes place in the context of Grid computing and, in particular, in the Client–Agent(s)–Server(s) model, where data can already be distributed on the platform. For each of those algorithms, we propose two mixed-parallel implementations. The former follows the phases of the original algorithms while the latter has been designed as the result of a list scheduling algorithm. We give a theoretical comparison, in terms of memory usage and execution time, between our algorithms and classical data-parallel implementations. This analysis is corroborated by experiments. Finally, we give some hints about heterogeneous and recursive versions of our algorithms. Copyright © 2004 John Wiley & Sons, Ltd.