PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs

Download Now
Provided by: Carnegie Mellon University
Topic: Storage
Format: PDF
Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly skewed power-law degree distributions, which challenge the assumptions made by these abstractions, limiting performance and scalability. In this paper, the authors characterize the challenges of computation on natural graphs in the context of existing graph parallel abstractions.
Download Now

Find By Topic