Parallel machine learning on big data Page 1 XRDS • fall 2012 • Vol.19 • No.1 60 On
algorithms for parallel machine learning, and why they need to be more efficient. By John …

This paper presents the design, implementation, and evaluation of the PyTorch distributed
data parallel module. PyTorch is a widely-adopted scientific computing package used in …

Literature has always witnessed efforts that make use of parallel algorithms/parallel
architecture to improve performance; machine learning space is no exception. In fact, a …

Machine learning models have achieved remarkable success in various real-world
applications such as data science, computer vision, and natural language processing …

Machine Learning (ML) techniques are indispensable in a wide range of fields.
Unfortunately, the exponential increase of dataset sizes are rapidly extending the runtime of …

Abstract Machine Learning methods have found increasing applicability and relevance to
the real world, finding applications in a broad range of fields in robotics, data mining, physics …

While high-level data parallel frameworks, like MapReduce, simplify the design and
implementation of large-scale data processing systems, they do not naturally or efficiently …

The landscape of machine learning applications is changing rapidly: large centralized
datasets are replaced by high volume, high velocity data streams generated by a vast …

Designing and implementing efficient, provably correct parallel machine learning (ML)
algorithms is challenging. Existing high-level parallel abstractions like MapReduce are …

In many ways, the objective of the IBM Parallel Machine Learning Toolbox (PML) is similar to
that of Google's MapReduce programming model (Dean and Ghemawat, 2004) and the …