# Dynamic Algorithms for the Massively Parallel Computation Model

## TL;DR

This paper extends the Massive Parallel Computing (MPC) model to handle dynamic, evolving datasets by developing efficient algorithms for fundamental graph problems, bridging the gap between static models and real-world data scenarios.

## Contribution

It introduces a dynamic MPC model, adapts classic dynamic algorithms to this setting, and presents new efficient algorithms for key graph problems.

## Key findings

- Developed a dynamic MPC model capturing real-world data evolution.
- Connected classic dynamic algorithms with the MPC framework.
- Provided efficient algorithms for connectivity, MST, and matching in dynamic MPC.

## Abstract

The Massive Parallel Computing (MPC) model gained popularity during the last decade and it is now seen as the standard model for processing large scale data. One significant shortcoming of the model is that it assumes to work on static datasets while, in practice, real-world datasets evolve continuously. To overcome this issue, in this paper we initiate the study of dynamic algorithms in the MPC model. We first discuss the main requirements for a dynamic parallel model and we show how to adapt the classic MPC model to capture them. Then we analyze the connection between classic dynamic algorithms and dynamic algorithms in the MPC model. Finally, we provide new efficient dynamic MPC algorithms for a variety of fundamental graph problems, including connectivity, minimum spanning tree and matching.