Hypergraph partitioning for multiple communication cost metrics: Model and methods

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

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

We investigate hypergraph partitioning-based methods for efficient paralleliza-tion of communicating tasks. A good partitioning method should divide the load among the processors as evenly as possible and minimize the inter-processor communication overhead. The total communication volume is the most popular communication overhead metric which is reduced by the existing state-of-the-art hypergraph partitioners. However, other metrics such as the total number of messages, the maximum amount of data transferred by a processor, or a combination of them are equally, if not more, important. Existing hypergraph-based solutions use a two phase approach to minimize such metrics where in each phase, they minimize a different metric, sometimes at the expense of others. We propose a one-phase approach where all the communication cost metrics can be effectively minimized in a multi-objective setting and reductions can be achieved for all metrics together. For an accurate modeling of the maximum volume and the number of messages sent and received by a processor, we propose the use of directed hypergraphs. The directions on hyperedges necessitate revisiting the standard partitioning heuristics. We do so and propose a multi-objective, multi-level hypergraph partitioner called UMPa. The partitioner takes various prioritized communication metrics into account, and optimizes all of them together in the same phase. Compared to the state-of-the-art methods which only minimize the total communication volume, we show on a large number of problem instances that UMPa produces better partitions in terms of several communication metrics.

Domaines: Calcul parallèle, distribué et partagé [cs.DC]; Mathématique discrète [cs.DM]; Algorithme et structure de données [cs.DS]

Mehmet Deveci, Kamer Kaya, Bora Uçar, Umit V. Catalyurek. Hypergraph partitioning for multiple communication cost metrics: Model and methods. Journal of Parallel and Distributed Computing, 2015, 77, pp.69--83. ⟨10.1016/j.jpdc.2014.12.002⟩. ⟨hal-01159676⟩