eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2019-11-28
33:1
33:16
10.4230/LIPIcs.ISAAC.2019.33
article
The I/O Complexity of Hybrid Algorithms for Square Matrix Multiplication
De Stefani, Lorenzo
1
Department of Computer Science, Brown University, United States of America
Asymptotically tight lower bounds are derived for the I/O complexity of a general class of hybrid algorithms computing the product of n x n square matrices combining "Strassen-like" fast matrix multiplication approach with computational complexity Theta(n^{log_2 7}), and "standard" matrix multiplication algorithms with computational complexity Omega (n^3). We present a novel and tight Omega ((n/max{sqrt M, n_0})^{log_2 7}(max{1,(n_0)/M})^3M) lower bound for the I/O complexity of a class of "uniform, non-stationary" hybrid algorithms when executed in a two-level storage hierarchy with M words of fast memory, where n_0 denotes the threshold size of sub-problems which are computed using standard algorithms with algebraic complexity Omega (n^3).
The lower bound is actually derived for the more general class of "non-uniform, non-stationary" hybrid algorithms which allow recursive calls to have a different structure, even when they refer to the multiplication of matrices of the same size and in the same recursive level, although the quantitative expressions become more involved. Our results are the first I/O lower bounds for these classes of hybrid algorithms. All presented lower bounds apply even if the recomputation of partial results is allowed and are asymptotically tight.
The proof technique combines the analysis of the Grigoriev’s flow of the matrix multiplication function, combinatorial properties of the encoding functions used by fast Strassen-like algorithms, and an application of the Loomis-Whitney geometric theorem for the analysis of standard matrix multiplication algorithms. Extensions of the lower bounds for a parallel model with P processors are also discussed.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol149-isaac2019/LIPIcs.ISAAC.2019.33/LIPIcs.ISAAC.2019.33.pdf
I/O complexity
Hybrid Algorithm
Matrix Multiplication
Recomputation