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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Parallel streaming graph partitioning utilizing multilevel framework

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      Embargo Lift Date: 2020-08-13
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      Author(s)
      Jafari, Nazanin
      Advisor
      Aykanat, Cevdet
      Date
      2018-08
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      Graph partitioning is widely used for e cient parallelization of a variety of applications. Streaming graph partitioning is a one pass partitioning solution provided to overcome high computation costs of o ine graph partitioners. Even though these streaming algorithms can be used for successively repartitioning, aiming at further improvements in partitioning qualities, quality improvements is limited to few passes that make o ine graph partitioning tools still a desirable solution for graph partitioning due to the generated high quality partitions. We propose a multilevel approach using streaming algorithms that can alleviate tradeo between quality and performance in graph partitioning problem. Moreover, our OpenMP based multi-threaded implementation, can generate fast and highly scalable solutions compared to mt-metis, a multi-threaded solution for METIS, the state-of-the-art o ine high quality graph partitioning tool. Our results show that our method can produce up to fteen times faster and more scalable results in large graph datasets. We also show that our method can improve quality of partitions signi cantly compared to state-of-the-art streaming graph partitioning algorithm LDG after repartitioning several times. On average we produce partitions with 29% better qualities than LDG algorithm.
      Keywords
      Streaming Graph Partitioning
      Parallel Computing
      Combinatorial Scientific Computing
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      http://hdl.handle.net/11693/47750
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      • Dept. of Computer Engineering - Master's degree 540
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