Now showing items 1-6 of 6

    • Application of map/reduce paradigm in supercomputing systems 

      Demirci, Gündüz Vehbi (Bilkent University, 2013)
      Map/Reduce is a framework first introduced by Google in order to rapidly develop big data analytic applications on distributed computing systems. Even though the Map/Reduce paradigm had a game changing impact on certain ...
    • Cartesian partitioning models for 2D and 3D parallel SpGEMM algorithms 

      Demirci, Gündüz Vehbi; Aykanat, Cevdet (IEEE, 2020)
      The focus is distributed-memory parallelization of sparse-general-matrix-multiplication (SpGEMM). Parallel SpGEMM algorithms are classified under one-dimensional (1D), 2D, and 3D categories denoting the number of dimensions ...
    • Cascade-aware partitioning of large graph databases 

      Demirci, Gündüz Vehbi; Ferhatosmanoğlu, H.; Aykanat, Cevdet (Springer, 2019)
      Graph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database ...
    • Locality-aware and load-balanced static task scheduling for MapReduce 

      Selvitopu, Oğuz; Demirci, Gündüz Vehbi; Türk, Ata; Aykanat, Cevdet (Elsevier, 2018)
      Task scheduling for MapReduce jobs has been an active area of research with the objective of decreasing the amount of data transferred during the shuffle phase via exploiting data locality. In the literature, generally ...
    • Partitioning models for scaling distributed graph computations 

      Demirci, Gündüz Vehbi (Bilkent University, 2019-08)
      The focus of this thesis is intelligent partitioning models and methods for scaling the performance of parallel graph computations on distributed-memory systems. Distributed databases utilize graph partitioning to provide ...
    • Scaling sparse matrix-matrix multiplication in the accumulo database 

      Demirci, Gündüz Vehbi; Aykanat, Cevdet (Springer, 2020)
      We propose and implement a sparse matrix-matrix multiplication (SpGEMM) algorithm running on top of Accumulo’s iterator framework which enables high performance distributed parallelism. The proposed algorithm provides ...