On Parallel k-Center Clustering

We consider the classic $k$-center problem in a parallel setting, on the low-local-space Massively Parallel Computation (MPC) model, with local space per machine of $\mathcal{O}(n^{\delta})$, where $\delta \in (0,1)$ is an arbitrary constant. As a central clustering problem, the $k$-center problem has been studied extensively. Still, until very recently, all parallel MPC algorithms have been requiring $\Omega(k)$ or even $\Omega(k n^{\delta})$ local space per machine. While this setting covers the case of small values of $k$, for a large number of clusters these algorithms require large local memory, making them poorly scalable. The case of large $k$, $k \ge \Omega(n^{\delta})$, has been considered recently for the low-local-space MPC model by Bateni et al. (2021), who gave an $\mathcal{O}(\log \log n)$-round MPC algorithm that produces $k(1+o(1))$ centers whose cost has multiplicative approximation of $\mathcal{O}(\log\log\log n)$. In this paper we extend the algorithm of Bateni et al. and design a low-local-space MPC algorithm that in $\mathcal{O}(\log\log n)$ rounds returns a clustering with $k(1+o(1))$ clusters that is an $\mathcal{O}(\log^{*n)$-approximation} for $k$-center.