TUX²: Distributed graph computation for machine learning

TUX² is a new distributed graph engine that bridges graph computation and distributed machine learning. TUX² inherits the benefits of an elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs; we extend and optimize it for distributed machine learning to support heterogeneity, a Stale Synchronous Parallel model, and a new MEGA (Mini-batch, Exchange, GlobalSync, and Apply) model. We have developed a set of representative distributed machine learning algorithms in TUX², covering both supervised and unsupervised learning. Compared to implementations on distributed machine learning platforms, writing these algorithms in TUX² takes only about 25% of the code: Our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. Our extensive evaluation of TUX², using large data sets with up to 64 billion edges, shows that TUX² outperforms state-of-the-art distributed graph engines PowerGraph and PowerLyra by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 48%.