  Linear solvers for large and sparse systems are a key element of scientific
applications, and their efficient implementation is necessary to harness the
computational power of current computers. Algebraic MultiGrid (AMG)
preconditioners are a popular ingredient of such linear solvers; this is the
motivation for the present work where we examine some recent developments in a
package of AMG preconditioners to improve efficiency, scalability, and
robustness on extreme-scale problems. The main novelty is the design and
implementation of a parallel coarsening algorithm based on aggregation of
unknowns employing weighted graph matching techniques; this is a completely
automated procedure, requiring no information from the user, and applicable to
general symmetric positive definite (s.p.d.) matrices. The new coarsening
algorithm improves in terms of numerical scalability at low operator complexity
over decoupled aggregation algorithms available in previous releases of the
package. The preconditioners package is built on the parallel software
framework \texttt{PSBLAS}, which has also been updated to progress towards
exascale. We present weak scalability results on one of the most powerful
supercomputers in Europe, for linear systems with sizes up to $O(10^{10})$
unknowns.
