  Pallalel GPGPU computing for lattice QCD simulations has a bottleneck on the
GPU to GPU data communication due to the lack of the direct data exchanging
facility. In this work we investigate the performance of quark solver using the
restricted additive Schwarz (RAS) preconditioner on a low cost GPU cluster. We
expect that the RAS preconditioner with appropriate domaindecomposition and
task distribution reduces the communication bottleneck. The GPU cluster we
constructed is composed of four PC boxes, two GPU cards are attached to each
box, and we have eight GPU cards in total. The compute nodes are connected with
rather slow but low cost Gigabit-Ethernet. We include the RAS preconditioner in
the single-precision part of the mixedprecision nested-BiCGStab algorithm and
the single-precision task is distributed to the multiple GPUs. The benchmarking
is done with the O(a)-improved Wilson quark on a randomly generated gauge
configuration with the size of $32^4$. We observe a factor two improvment on
the solver performance with the RAS precoditioner compared to that without the
preconditioner and find that the improvment mainly comes from the reduction of
the communication bottleneck as we expected.
