Some recent papers showed that many sequential iterative algorithms can be directly
parallelized, by identifying the dependences between the input objects. This approach …

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 expressive power of the Lara language–a recently proposed unified model for
expressing relational and linear algebra operations–both in terms of traditional database …

Sparse pooling methods for graph neural networks typically perform graph reduction by
keeping only the top-k vertices according to an adaptive scoring function. Although fast and …

Communication complexity is an area of computational complexity theory that studies the
amount of communication required to complete a computational task. Communication …

This book is an introduction to the field of parallel algorithms and the underpinning
techniques to realize the parallelization. The emphasis is on designing algorithms within the …

There are two goals in designing efficient parallel algorithms: to reduce work, and to improve
parallelism. Work-efficiency, meaning that the work (total number of operations) is …

The chapter presents what would in a nutshell be a traveling through time with the problem
of Feedback Arc Set. The review goes from the first paper, jumping through a number of …

Distributed computing studies models in which multiple computational units coordinate
themselves to work towards some common goal while having only limited resources at their …

As the scale of the problems we want to solve in real life becomes larger, the input sizes of
the problems we want to solve could be much larger than the memory of a single computer …