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

The goal of this article is to identify fundamental limitations on how efficiently algorithms
implemented on platforms such as MapReduce and Hadoop can compute the central …

In this paper we develop optimal algorithms in the binary-forking model for a variety of
fundamental problems, including sorting, semisorting, list ranking, tree contraction, range …

Abstract The Massively Parallel Computation (MPC) model serves as a common abstraction
of many modern large-scale data processing frameworks, and has been receiving …

We study the maximum $ k $-set coverage problem in the following distributed setting. A
collection of sets $ S_1,\ldots, S_m $ over a universe $[n] $ is partitioned across $ p …

Industry-grade database systems are expected to produce the same result if the same query
is repeatedly run on the same input. However, the numerous sources of non-determinism in …

Abstract We introduce the Read-Only Semi-External (ROSE) Model for the design and
analysis of algorithms on large graphs. As in the well-studied semi-external model for graph …

MapReduce (and its open source implementation Hadoop) has become the de facto
platform for processing large data sets. MapReduce offers a streamlined computational …

Relational Aggregation is one of the major means to analyze large data sets since the
creation of the first database systems. Available hardware performance continues to grow at …

We present a randomized algorithm which computes, for any fixed k, a tree decomposition of
width at most k of any input graph. We analyze it in the parallel external memory (PEM) …