BGRAP:
Balanced GRAph Partitioning Algorithm for Large Graphs (pp116-135)
Adnan El Moussawi,
Nacera Bennacer Seghouani, and Francesca Bugiotti
doi:
https://doi.org/10.26421/JDI2.2-2
Abstracts:
The
definition of effective strategies for graph partitioning is a major
challenge in distributed environments since an effective graph
partitioning allows to considerably improve the performance of large
graph data analytics computations. In this paper, we propose a
multi-objective and scalable Balanced GRAph
Partitioning (\algo)
algorithm, based on Label Propagation (LP) approach, to produce
balanced graph partitions. \algo
defines a new efficient initialization procedure and different
objective functions to deal with either vertex or edge balance
constraints while considering edge direction in graphs.
\algo
is implemented of top of the open source distributed graph
processing system Giraph. The
experiments are performed on various graphs with different
structures and sizes (going up to 50.6M vertices and 1.9B
edges) while varying the number of partitions. We evaluate
\algo
using several quality measures and the computation time. The results
show that \algo
(i) provides a good balance while reducing the cuts between the
different computed partitions (ii) reduces the global computation
time, compared to LP-based algorithms.
Key words:
graph
partitioning, label propagation, vertex balance, edge balance,
vertex-centric parallel computing, Giraph