Fast, General Parallel Computation for Machine Learning

Abstract

Today the terms machine learning (ML) and Big Data are closely correlated. This, and the complexity of many ML algorithms, motivates a search for fast parallel computation methods. A further motivating factor is a need to deal with memory size limitations, especially for the moderately-sized machines common in many ML applications. In addition, it is desirable to develop generally applicable methods, rather than needing to develop a different parallel approach for every ML algorithm. In this work, we apply a technique we call Software Alchemy to ML. We are particularly interested in ML for recommender systems, and explore the feasibility of SA in that context.