Multithreaded clustering for multi-level hypergraph partitioning

Abstract : Requirements for efficient parallelization of many complex and irregular applications can be cast as a hyper graph partitioning problem. The current-state-of-the art software libraries that provide tool support for the hyper graph partitioning problem are designed and implemented before the game-changing advancements in multi-core computing. Hence, analyzing the structure of those tools for designing multithreaded versions of the algorithms is a crucial tasks. The most successful partitioning tools are based on the multi-level approach. In this approach, a given hyper graph is coarsened to a much smaller one, a partition is obtained on the the smallest hyper graph, and that partition is projected to the original hyper graph while refining it on the intermediate hyper graphs. The coarsening operation corresponds to clustering the vertices of a hyper graph and is the most time consuming task in a multi-level partitioning tool. We present three efficient multithreaded clustering algorithms which are very suited for multi-level partitioners. We compare their performance with that of the ones currently used in today's hyper graph partitioners. We show on a large number of real life hyper graphs that our implementations, integrated into a commonly used partitioning library PaToH, achieve good speedups without reducing the clustering quality.
Type de document :
Communication dans un congrès
26th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2012,, May 2012, Shanghai, China. IEEE Computer Society, pp.848--859, 2012, 〈10.1109/IPDPS.2012.81〉
Liste complète des métadonnées

https://hal.inria.fr/hal-00763565
Contributeur : Equipe Roma <>
Soumis le : mardi 11 décembre 2012 - 10:12:33
Dernière modification le : vendredi 20 avril 2018 - 15:44:27

Identifiants

Collections

Citation

Umit Catalyurek, Mehmet Deveci, Kamer Kaya, Bora Uçar. Multithreaded clustering for multi-level hypergraph partitioning. 26th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2012,, May 2012, Shanghai, China. IEEE Computer Society, pp.848--859, 2012, 〈10.1109/IPDPS.2012.81〉. 〈hal-00763565〉

Partager

Métriques

Consultations de la notice

182