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Distributed Graph Computation Meets Machine Learning
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DOI：10.1109/TPDS.2020.2970047.png)
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Abstract
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TuX(2) is a new distributed graph engine that bridges graph computation and distributed machine learning. TuX(2) inherits the benefits of elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs, while extended and optimized for distributed machine learning to support heterogeneity in data model, Stale Synchronous Parallel in scheduling, and a new Mini-batch, Exchange, GlobalSync, and Apply (MEGA) model for programming. TuX(2) further introduces a hybrid vertex-cut graph optimization and supports various consistency models in fault tolerance for machine learning. We have developed a set of representative distributed machine learning algorithms in TuX(2), covering both supervised and unsupervised learning. Compared to the implementations on distributed machine learning platforms, writing those algorithms in TuX(2) takes only about 25 percent of the code: our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. The extensive evaluation of TuX(2), using large datasets with up to 64 billion of edges, shows that TuX(2) outperforms PowerGraph/PowerLyra, the state-of-the-art distributed graph engines, by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 60 percent.
Keywords:
Graph computing
distributed machine learning
heterogeneity
stale synchronous parallel
MEGA model
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