Massively Parallel Minimum Spanning Tree in General Metric Spaces

We study the minimum spanning tree (MST) problem in the massively parallel computation (MPC) model. Our focus is particularly on the *strictly sublinear* regime of MPC where the space per machine is O(nδ). Here n is the number of vertices and constant δ ∈ (0, 1) can be made arbitrarily small. The MST problem admits a simple and folklore O(log n)-round algorithm in the MPC model. When the weights can be arbitrary, this matches a conditional lower bound of Omega(log n) which follows from a well-known 1vs2-Cycle conjecture. As such, much of the literature focuses on breaking the logarithmic barrier in more structured variants of the problem, such as when the vertices correspond to points in low- [ANOY14, STOC'14] or high-dimensional Euclidean spaces [JMNZ, SODA'24]. In this work, we focus more generally on metric spaces. Namely, all pairwise weights are provided and guaranteed to satisfy the triangle inequality, but are otherwise unconstrained. We show that for any varepsilon > 0, a (1+varepsilon)-approximate MST can be found in O(log frac1varepsilon + log log n) rounds, which is the first o(log n)-round algorithm for finding any constant approximation in this setting. Other than being applicable to more general weight functions, our algorithm also slightly improves the O(log log n · log log log n) round-complexity of [JMNZ24, SODA'24] and significantly improves its approximation from a large constant to 1+varepsilon. On the lower bound side, we prove that under the 1vs2-Cycle conjecture, Omega(log frac1varepsilon) rounds are needed for finding a (1+varepsilon)-approximate MST in general metrics. It is worth noting that while many existing lower bounds in the MPC model under the 1vs2-Cycle conjecture only hold against "component stable" algorithms, our lower bound applies to *all* algorithms.