On analysis of partitioning models and metrics in parallel sparse matrix-vector multiplication

Umit V. Catalyurek (1) , Kamer Kaya (1) , Bora Uçar (2, 3)

1 Department of Biomedical Informatics [Columbus]
2 LIP - Laboratoire de l'Informatique du Parallélisme
3 ROMA - Optimisation des ressources : modèles, algorithmes et ordonnancement

Graph/hypergraph partitioning models and methods have been successfully used to minimize the communication requirements among processors in several parallel computing applications. Parallel sparse matrix-vector multiplication~(SpMxV) is one of the representative applications that renders these models and methods indispensable in many scientific computing contexts. We investigate the interplay of several partitioning metrics and execution times of SpMxV implementations in three libraries: Trilinos, PETSc, and an in-house one. We design and carry out experiments with up to 512 processors and investigate the results with regression analysis. Our experiments show that the partitioning metrics, although not an exact measure of communication cost, influence the performance greatly in a distributed memory setting. The regression analyses demonstrate which metric is the most influential for the execution time of the three libraries used.

Mots clés: Parallel sparse-matrix vector multiplication; hypergraph partitioning

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Umit V. Catalyurek, Kamer Kaya, Bora Uçar. On analysis of partitioning models and metrics in parallel sparse matrix-vector multiplication. [Research Report] RR-8301, INRIA. 2013, pp.25. ⟨hal-00821523v3⟩