  The study of approximate matching in the Massively Parallel Computations
(MPC) model has recently seen a burst of breakthroughs. Despite this progress,
however, we still have a far more limited understanding of maximal matching
which is one of the central problems of parallel and distributed computing. All
known MPC algorithms for maximal matching either take polylogarithmic time
which is considered inefficient, or require a strictly super-linear space of
$n^{1+\Omega(1)}$ per machine.
  In this work, we close this gap by providing a novel analysis of an extremely
simple algorithm a variant of which was conjectured to work by Czumaj et al.
[STOC'18]. The algorithm edge-samples the graph, randomly partitions the
vertices, and finds a random greedy maximal matching within each partition. We
show that this algorithm drastically reduces the vertex degrees. This, among
some other results, leads to an $O(\log \log \Delta)$ round algorithm for
maximal matching with $O(n)$ space (or even mildly sublinear in $n$ using
standard techniques).
  As an immediate corollary, we get a $2$ approximate minimum vertex cover in
essentially the same rounds and space. This is the best possible approximation
factor under standard assumptions, culminating a long line of research. It also
leads to an improved $O(\log\log \Delta)$ round algorithm for $1 + \varepsilon$
approximate matching. All these results can also be implemented in the
congested clique model within the same number of rounds.

Data Structures and Algorithms;Distributed, Parallel, and Cluster Computing