Cataloged 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.by Mestan Fırat Çeliktuğ.M.S
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