The GraphLab project started in 2009 to develop a new parallel computation abstraction tailored to machine learning. GraphLab 1.0 represents our first shared memoy design which, through the addition of several matrix factorization toolkits contributed by our post-doc Danny Bickson, started to grow a community of users.

In the last couple of years, we have focused our development effort on the distributed environment. Unfortunately, it took nearly a year to figure out that distributing the GraphLab 1 abstraction was excessively complicated and is unable to scale up to power-law graphs commonly seen in the real world.

GraphLab 2.1 represents the latest evolution of the GraphLab abstraction and is a complete redesign of the GraphLab 1 framework for the distributed environment. The implementation is distributed by design and a "shared-memory" execution is essentially running a distributed system on a cluster of 1 machine. Not all toolkits from GraphLab 1 have been ported over yet; some complex algorithms may take some time.

There are two starting points where one may begin using GraphLab.

Toolkits You can lookup the toolkit documentation here if you have a computation task which is already implemented by one of our toolkits.
GraphLab C++ Tutorial If you have a computation task which is not implemented by our toolkits, you could try implementing yourself! For now a certain degree of C++ knowledge is required. However, we are trying to provide an interface to other languages such as Javascript and Python. Contact the developers (here) if you want to beta-test these interfaces, or come back in a couple of months when we may have something stable.

Software Stack