  We propose a generalized Langevin dynamics (GLD) technique to construct
non-Markovian particle-based coarse-grained models from fine-grained reference
simulations and to efficiently integrate them. The proposed GLD model has the
form of a discretized generalized Langevin equation with distance-dependent
two-particle contributions to the self- and pair-memory kernels. The memory
kernels are iteratively reconstructed from the dynamical correlation functions
of an underlying fine-grained system. We develop a simulation algorithm for
this class of non-Markovian models that scales linearly with the number of
coarse-grained particles. Our GLD method is suitable for coarse-grained studies
of systems with incomplete time scale separation, as is often encountered,
e.g., in soft matter systems.
  We apply the method to a suspension of nanocolloids with frequency-dependent
hydrodynamic interactions. We show that the results from GLD simulations
perfectly reproduce the dynamics of the underlying fine-grained system. The
effective speedup of these simulations amounts to a factor of about $10^4$.
Additionally, the transferability of the coarse-grained model with respect to
changes of the nanocolloid density is investigated. The results indicate that
the model is transferable to systems with nanocolloid densities that differ by
up to one order of magnitude from the density of the reference system.
