
244 Large Scale and Big Data
using a sequential and high-quality partitioning algorithm such as GGGP (Greedy
Graph Growing Partitioning) [45]. In the uncoarsening phase, the partitions are
then iteratively projected back toward the original graph, with a local renement
on each iteration.
The iterations are highly parallelizable, and their efciency and scalability has
been evaluated on shared-memory architectures (such as Cray supercomputers)
[44,46]. However, in the coarsening and uncoarsening phases, all the edges may be
accessed, generating a lot of network trafc if the input graph is stored in distributed
machines.
7.5.3.2 Network Trans ...