Algorithms for a Topology-aware Massively Parallel Computation Model

Abstract

Most of the prior work in massively parallel data processing assumes homogeneity, i.e., every computing unit has the same computational capability and can communicate with every other unit with the same latency and bandwidth. However, this strong assumption of a uniform topology rarely holds in practical settings, where computing units are connected through complex networks. To address this issue, Blanas et al. \citeblanas2020topology recently proposed a topology-aware massively parallel computation model that integrates the network structure and heterogeneity in the modeling cost. The network is modeled as a directed graph, where each edge is associated with a cost function that depends on the data transferred between the two endpoints. The computation proceeds in synchronous rounds and the cost of each round is measured as the maximum cost over all the edges in the network. In this work, we take the first step into investigating three fundamental data processing tasks in this topology-aware parallel model: set intersection, cartesian product, and sorting. We focus on network topologies that are tree topologies, and present both lower bounds as well as (asymptotically) matching upper bounds. Instead of assuming a worst-case distribution as in previous results, the optimality of our algorithms is with respect to the initial data distribution among the network nodes. Apart from the theoretical optimality of our results, our protocols are simple, use a constant number of rounds, and we believe can be implemented in practical settings as well.