Graph/hypergraph partitioning models for simultaneous load balancing on computation and data

In the literature, several successful partitioning models and methods have been proposed and used for computational load balancing of irregularly sparse applications on distributed-memory architectures. However, the literature lacks partitioning models and methods that encode both computational and data load balancing of processors. In this thesis, we try to close this gap by proposing graph and hypergraph partitioning models and methods that simultaneously encode computational and data load balancing of processors. The validity of the proposed models and methods are tested on two widely-used irregularly sparse applications: parallel mesh simulations and parallel sparse matrix sparse matrix multiplication.