In the last two decades, the continuous increase of computational power has produced an
overwhelming flow of data which has called for a paradigm shift in the computing …

X-Stream is a system for processing both in-memory and out-of-core graphs on a single
shared-memory machine. While retaining the scatter-gather programming model with state …

GPS (for Graph Processing System) is a complete open-source system we developed for
scalable, fault-tolerant, and easy-to-program execution of algorithms on extremely large …

Machine learning (ML) and statistical techniques are key to transforming big data into
actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML …

Breadth‐First Search is an important kernel used by many graph‐processing applications. In
many of these emerging applications of BFS, such as analyzing social networks, the input …

MLI is an Application Programming Interface designed to address the challenges of building
Machine Learning algorithms in a distributed setting based on data-centric computing. Its …

Scaling large iterative graph processing applications through parallel computing is a very
important problem. Several graph processing frameworks have been proposed that insulate …

Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized
loss minimization problems in machine learning. This paper considers an extension of …

It is cumbersome to write machine learning and graph algorithms in data-parallel models
such as MapReduce and Dryad. We observe that these algorithms are based on matrix …

Myriad of graph-based algorithms in machine learning and data mining require parsing
relational data iteratively. These algorithms are implemented in a large-scale distributed …