  We show that using the multi-splitting algorithm as a preconditioner for the
domain wall Dirac linear operator, arising in lattice QCD, effectively reduces
the inter-node communication cost, at the expense of performing more on-node
floating point and memory operations. Correctly including the boundary
\textit{snake} terms, the preconditioner is implemented in the QUDA framework,
where it is found that utilizing kernel fusion and the tensor cores on NVIDIA
GPUs is necessary to achieve a sufficiently performant preconditioner. A
reduced-dimension (reduced-$L_s$) strategy is also proposed and tested for the
preconditioner. We find the method achieves lower time to solution than regular
CG at high node count despite the additional local computational requirements
from the preconditioner. This method could be useful for supercomputers with
more on-node flops and memory bandwidth than inter-node communication
bandwidth.
