  We show feasibility and benefits of porting an adaptive multi-scale
kinetic-fluid code to CPU-GPU systems. Challenges are due to the irregular data
access for adaptive Cartesian mesh, vast difference of computational cost
between kinetic and fluid cells, and desire to evenly load all CPUs and GPUs.
Our Unified Flow Solver (UFS) combines Adaptive Mesh Refinement (AMR) with
automatic cell-by-cell selection of kinetic or fluid solvers based on continuum
breakdown criteria. Using GPUs enables hybrid simulations of mixed
rarefied-continuum flows with a million of Boltzmann cells with 24x24x24
velocity mesh. We describe the implementation of CUDA kernels for three modules
in UFS: the direct Boltzmann solver using discrete velocity method, the Direct
Simulation Monte Carlo (DSMC) solver, and a mesoscopic solver based on Lattice
Boltzmann Method, all using octree Cartesian mesh. Double digit speedups on
single GPU and good scaling for multi-GPUs have been demonstrated.
