  The gap between the cost of moving data and the cost of computing continues
to grow, making it ever harder to design iterative solvers on extreme-scale
architectures. This problem can be alleviated by alternative algorithms that
reduce the amount of data movement. We investigate this in the context of
Lattice Quantum Chromodynamics and implement such an alternative solver
algorithm, based on domain decomposition, on Intel Xeon Phi co-processor (KNC)
clusters. We demonstrate close-to-linear on-chip scaling to all 60 cores of the
KNC. With a mix of single- and half-precision the domain-decomposition method
sustains 400-500 Gflop/s per chip. Compared to an optimized KNC implementation
of a standard solver [1], our full multi-node domain-decomposition solver
strong-scales to more nodes and reduces the time-to-solution by a factor of 5.
