The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

We study the problem of computing conjunctive queries over large databases on parallel
architectures without shared storage. Using the structure of such a query q and the skew in …

The algorithms community has been modeling the underlying key features and constraints of
massively parallel frameworks and using these models to discover new algorithmic …

Greedy algorithms are practitioners' best friends—they are intuitive, are simple to implement,
and often lead to very good solutions. However, implementing greedy algorithms in a …

We give algorithms for geometric graph problems in the modern parallel models such as
MapReduce. For example, for the Minimum Spanning Tree (MST) problem over a set of …

Any graph with maximum degree Δ admits a proper vertex coloring with Δ+ 1 colors that can
be found via a simple sequential greedy algorithm in linear time and space. But can one find …

Graph clustering is a fundamental task in many data-mining and machine-learning
pipelines. In particular, identifying a good hierarchical structure is at the same time a …

We present O (loglog n)-round algorithms in the Massively Parallel Computation (MPC)
model, with Õ (n) memory per machine, that compute a maximal independent set, a 1+ ε …

Many modern parallel systems, such as MapReduce, Hadoop and Spark, can be modeled
well by the MPC model. The MPC model captures well coarse-grained computation on large …

There is a rapidly growing need for scalable algorithms that solve classical graph problems,
such as maximum matching and minimum vertex cover, on massive graphs. For massive …