Çeliktuğ, Mestan Fırat2019-01-092019-01-092018-122018-122019-01-04http://hdl.handle.net/11693/48228Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.Includes bibliographical references (leaves 57-64).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.xii, 72 leaves : illustrations (some color), charts (some color) ; 30 cm.Englishinfo:eu-repo/semantics/openAccessComputation Load BalancingData Load BalancingSimultaneous Computation And Data Load BalancingMulti-Constraint Graph PartitioningMulticonstraint Hypergraph PartitioningThesisB159511