Buffered Streaming Graph Partitioning
Marcelo Fonseca Faraj, Christian Schulz

TL;DR
This paper introduces a buffered streaming graph partitioning algorithm that improves partition quality on large graphs using minimal memory, bridging the gap between fast streaming and high-quality offline methods.
Contribution
It presents a novel buffered streaming approach that employs multilevel algorithms on model graphs, significantly enhancing partition quality with low memory usage.
Findings
Achieves 75.9% better solutions than Fennel on average.
Removes dependency on number of blocks from runtime.
Faster than Fennel for large number of blocks.
Abstract
Partitioning graphs into blocks of roughly equal size is widely used when processing large graphs. Currently there is a gap in the space of available partitioning algorithms. On the one hand, there are streaming algorithms that have been adopted to partition massive graph data on small machines. In the streaming model, vertices arrive one at a time including their neighborhood and then have to be assigned directly to a block. These algorithms can partition huge graphs quickly with little memory, but they produce partitions with low solution quality. On the other hand, there are offline (shared-memory) multilevel algorithms that produce partitions with high quality but also need a machine with enough memory. We make a first step to close this gap by presenting an algorithm that computes significantly improved partitions of huge graphs using a single machine with little memory in…
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Taxonomy
TopicsCaching and Content Delivery · Graph Theory and Algorithms · Interconnection Networks and Systems
