Parallel streaming graph partitioning utilizing multilevel framework
Author(s)
Advisor
Date
2018-08Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
136
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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.