Lower bounds for sparse matrix vector multiplication on hypercubic networks - Inria - Institut national de recherche en sciences et technologies du numérique
Article Dans Une Revue Discrete Mathematics and Theoretical Computer Science Année : 1998

Lower bounds for sparse matrix vector multiplication on hypercubic networks

Résumé

In this paper we consider the problem of computing on a local memory machine the product y = Ax,where A is a random n×n sparse matrix with Θ (n) nonzero elements. To study the average case communication cost of this problem, we introduce four different probability measures on the set of sparse matrices. We prove that on most local memory machines with p processors, this computation requires Ω ((n/p) \log p) time on the average. We prove that the same lower bound also holds, in the worst case, for matrices with only 2n or 3n nonzero elements.
Fichier principal
Vignette du fichier
dm020103.pdf (164.2 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00958901 , version 1 (13-03-2014)

Identifiants

Citer

Giovanni Manzini. Lower bounds for sparse matrix vector multiplication on hypercubic networks. Discrete Mathematics and Theoretical Computer Science, 1998, Vol. 2, pp.35-47. ⟨10.46298/dmtcs.249⟩. ⟨hal-00958901⟩

Collections

TDS-MACS
86 Consultations
846 Téléchargements

Altmetric

Partager

More