Monday, October 7, 2013

New SOSP paper: a lightweight infrastructure for graph analytics

I got this reference from my collaborator Aapo Kyorla, author of GraphChi.

A Lightweight Infrastructure for Graph Analytics. Donald Nguyen, Andrew Lenharth, Keshav Pingali (University of Texas at Austin), to appear in SOSP 2013.

It is an interesting paper which heavily compares to GraphLab, PowerGraph (GraphLab v2.1) and

One of the main claims is that dynamic and asynchronous scheduling can significantly speed up many graph algorithms (vs. bulk synchronous parallel model where all graph nodes are executed on each step).

Some concerns I have is regarding the focus on multicore settings, which makes everything much easier, and thus to comparison with PowerGraph less relevant.

Another relevant paper which improves on GraphLab is: Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC. Zhiyuan Lin, Duen Horng Chau, and U Kang, IEEE Big Data Workshop: Scalable Machine Learning: Theory and Applications, 2013. The basic idea is to speed graph loading using mmap() operation.

1 comment:

  1. You might also be interested in this other paper at SOSP from us at EPFL. X-Stream: Edge-Centric Graph Processing using Streaming Partitions. We're focused on producing results directly from unstructured edge lists using semi-streaming techniques. A scale out version is currently being tested.
    Source code also released at: