  We present an MPI + OpenACC implementation of the kernel-independent
barycentric Lagrange treecode (BLTC) for fast summation of particle
interactions on GPUs. The distributed memory parallelization uses recursive
coordinate bisection for domain decomposition and MPI remote memory access to
build locally essential trees on each rank. The particle interactions are
organized into target batch/source cluster interactions which efficiently map
onto the GPU; target batching provides an outer level of parallelism, while the
direct sum form of the barycentric particle-cluster approximation provides an
inner level of parallelism. The GPU-accelerated BLTC performance is
demonstrated on several test cases up to 1~billion particles interacting via
the Coulomb potential and Yukawa potential.
