Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems

Abstract

Industrial-scale machine learning applications often train and maintain massive models that can be on the order of hundreds of millions to billions of parameters. Model parallelism thus plays a significant role to support these machine learning tasks. Recent work in this area has been dominated by parameter server architectures that follow an asynchronous computation model, introducing added complexity and approximation in order to scale to massive workloads. In this work, we explore model parallelism in the distributed bulk-synchronous parallel (BSP) setting, leveraging some recent progress made in the area of high performance computing, in order to address these complexity and approximation issues. Using collaborative filtering as a case-study, we introduce an efficient model parallel industrial scale algorithm for alternating least squares (ALS), along with a highly optimized implementation of ALS that serves as the default implementation in MLlib, Apache Spark's machine learning library. Our extensive empirical evaluation demonstrates that our implementation in MLlib compares favorably to the leading open-source parameter server framework, and our implementation scales to massive problems on the order of 50 billion ratings and close to 1 billion parameters.