Coupling-Aware Graph Partitioning Algorithms: Preliminary Study

Maria Predari (1, 2) , Aurélien Esnard (1, 2)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 HiePACS - High-End Parallel Algorithms for Challenging Numerical Simulations

In the field of scientific computing, load balancing is a major issue that determines the performance of parallel applications. Nowadays, simulations of real-life problems are becoming more and more complex, involving numerous coupled codes, representing different models. In this context, reaching high performance can be a great challenge. In this paper, we present graph partitioning techniques, called co-partitioning, that address the problem of load balancing for two coupled codes: the key idea is to perform a "coupling-aware" partitioning, instead of partitioning these codes independently, as it is usually done. Finally, we present a preliminary experimental study which compares our methods against the usual approach.

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

Maria Predari, Aurélien Esnard. Coupling-Aware Graph Partitioning Algorithms: Preliminary Study. IEEE International Conference on High Performance Computing (HiPC 2014), Dec 2014, Goa, India. ⟨10.1109/HiPC.2014.7116879⟩. ⟨hal-01069578⟩