Communication efficient matrix multiplication on hypercubes

By H. Gupta, Ponnuswamy Sadayappan. 1994

In this paper we present an efficient dense matrix multiplication algorithm for distributed memory computers with a hypercube topology. The proposed algorithm performs better than all previously proposed algorithms for a wide range of matrix sizes and number of processors, especially for large matrices. We analyze the performance of the algorithms for two types of hypercube architectures, one in which each node can use (to send and receive) at most one communication link at a time and the other in which each node can use all communication links simultaneously.

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Citations in the corpus

Communication lower bounds for distributed-memory matrix multiplication (2004, methodology): “Berntsen's algorithm is somewhat more complex than the rest.”
Communication-Optimal Parallel Recursive Rectangular Matrix Multiplication (2013, unknown): “Communication-Optimal Parallel Recursive Rectangular Matrix Multiplication James Demmel∗, David Eliahu†††, Armando Fox‡††, Shoaib Kamil§, Benjamin Lipshitz¶††, Oded Schwartz‖††, and Omer Spillinger∗∗†† Abstract—Communication-optimal algorithms are known for square matrix multiplication.”
Scalable task-based algorithm for multiplication of block-rank-sparse matrices (2015, background): “What they share in common is the reduced number of parameters in terms of which the data is represented, at the cost of more complex data representation and computation relative to the standard/näıve counterpart.”