  With this work, we release CLAIRE, a distributed-memory implementation of an
effective solver for constrained large deformation diffeomorphic image
registration problems in three dimensions. We consider an optimal control
formulation. We invert for a stationary velocity field that parameterizes the
deformation map. Our solver is based on a globalized, preconditioned, inexact
reduced space Gauss--Newton--Krylov scheme. We exploit state-of-the-art
techniques in scientific computing to develop an effective solver that scales
to thousands of distributed memory nodes on high-end clusters. We present the
formulation, discuss algorithmic features, describe the software package, and
introduce an improved preconditioner for the reduced space Hessian to speed up
the convergence of our solver. We test registration performance on synthetic
and real data. We demonstrate registration accuracy on several neuroimaging
datasets. We compare the performance of our scheme against different flavors of
the Demons algorithm for diffeomorphic image registration. We study convergence
of our preconditioner and our overall algorithm. We report scalability results
on state-of-the-art supercomputing platforms. We demonstrate that we can solve
registration problems for clinically relevant data sizes in two to four minutes
on a standard compute node with 20 cores, attaining excellent data fidelity.
With the present work we achieve a speedup of (on average) 5$\times$ with a
peak performance of up to 17$\times$ compared to our former work.

Optimization and Control;Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing;Numerical Analysis;Numerical Analysis