  Algebraic multigrid (AMG) is often viewed as a scalable $\mathcal{O}(n)$
solver for sparse linear systems. Yet, parallel AMG lacks scalability due to
increasingly large costs associated with communication, both in the initial
construction of a multigrid hierarchy as well as the iterative solve phase.
This work introduces a parallel implementation of AMG to reduce the cost of
communication, yielding an increase in scalability. Standard inter-process
communication consists of sending data regardless of the send and receive
process locations. Performance tests show notable differences in the cost of
intra- and inter-node communication, motivating a restructuring of
communication. In this case, the communication schedule takes advantage of the
less costly intra-node communication, reducing both the number and size of
inter-node messages. Node-centric communication extends to the range of
components in both the setup and solve phase of AMG, yielding an increase in
the weak and strong scalability of the entire method.
