  We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented
in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable
simulations. We implement and validate the accuracy of a novel multi-grid
scheme based on multiresolution pyramids to compute large scale forces
efficiently on distributed platforms. We explore the scaling of the simulation
on large-scale supercomputers and compare it with corresponding python based PM
code, finding on an average 10x speed-up in terms of wallclock time. We also
demonstrate how this novel tool can be used for efficiently solving large scale
cosmological inference problems, in particular reconstruction of cosmological
fields in a forward model Bayesian framework with hybrid PM and neural network
forward model. We provide skeleton code for these examples and the entire code
is publicly available at https://github.com/modichirag/flowpm.
