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      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Ph.D. / Sc.D.
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      Partitioning models for scaling distributed graph computations

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      Embargo Lift Date: 2020-03-05
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      Author
      Demirci, Gündüz Vehbi
      Advisor
      Aykanat, Cevdet
      Date
      2019-09
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      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 servers with data-locality and workload-balance. Some queries performed on a database may form cascades due to the queries triggering each other. The current partitioning methods consider the graph structure and logs of query workload. We introduce the cascade-aware graph partitioning problem with the objective of minimizing the overall cost of communication operations between servers during cascade processes. We propose a randomized algorithm that integrates the graph structure and cascade processes to use as input for large-scale partitioning. Experiments on graphs representing real social networks demonstrate the e ectiveness of the proposed solution in terms of the partitioning objectives. Sparse-general-matrix-multiplication (SpGEMM) is a key computational kernel used in scienti c computing and high-performance graph computations. We propose an SpGEMM algorithm for Accumulo database which enables high performance distributed parallelism through its iterator framework. The proposed algorithm provides write-locality and avoids scanning input matrices multiple times by utilizing Accumulo's batch scanning capability and node-level parallelism structures. We also propose a matrix partitioning scheme that reduces the total communication volume and provides a workload-balance among servers. Extensive experiments performed on both real-world and synthetic sparse matrices show that the proposed algorithm and matrix partitioning scheme provide signi cant performance improvements. Scalability of parallel SpGEMM algorithms are heavily communication bound. Multidimensional partitioning of SpGEMM's workload is essential to achieve higher scalability. We propose hypergraph models that utilize the arrangement of processors and also attain a multidimensional partitioning on SpGEMM's workload. Thorough experimentation performed on both realistic as well as synthetically generated SpGEMM instances demonstrates the e ectiveness of the proposed partitioning models.
      Keywords
      Graph partitioning
      Propagation models
      Information cascade
      Social networks
      Randomized algorithms
      Scalability
      Databases
      Accumulo
      Graphulo
      Parallel and distributed computing
      Sparse matrices
      Sparse matrix-matrix multiplication
      SpGEMM
      Matrix partitioning
      Data locality
      Hypergraph partitioning
      Numerical linear algebra
      Permalink
      http://hdl.handle.net/11693/52406
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      • Dept. of Computer Engineering - Ph.D. / Sc.D. 75
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