Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Efficient Data Partitioning Model for Heterogeneous Graphs in the Cloud
IEEE international Conference for High Performance Computing, Networking, Storage and Analysis (SC2013), November 2013.
Kisung Lee and Ling Liu
Georgia Institute of Technology
As the size and variety of information networks continue to grow in many scientific and engineering domains, we witness a growing demand for efficient processing of large heterogeneous graphs using a cluster of compute nodes in the Cloud. One open issue is how to effectively partition a large graph to process complex graph operations efficiently. In this paper, we present VB-Partitioner -- a distributed data partitioning model and algorithms for efficient processing of graph operations over large-scale graphs in the Cloud. Our VB-Partitioner has three salient features. First, it introduces vertex blocks (VBs) and extended vertex blocks (EVBs) as the building blocks for semantic partitioning of large graphs. Second, VB-Partitioner utilizes vertex block grouping algorithms to place those vertex blocks that have high correlation in graph structure into the same partition. Third, VB-Partitioner employs a VB-partition guided query partitioning model to speed up the parallel processing of graph pattern queries by reducing the amount of inter-partition query processing. We conduct extensive experiments on several real-world graphs with millions of vertices and billions of edges. Our results show that VB-Partitioner significantly outperforms the popular random block-based data partitioner in terms of query latency and scalability over large-scale graphs.
FULL PAPER: pdf