High-Performance Distributed ML at Scale through Parameter Server Consistency Models

29 Oct 2014Wei DaiAbhimanu KumarJinliang WeiQirong HoGarth GibsonEric P. Xing

As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Unfortunately, effective use of clusters for ML requires considerable expertise in writing distributed code, while highly-abstracted frameworks like Hadoop have not, in practice, approached the performance seen in specialized ML implementations... (read more)

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