  We provide details on the implementation of a machine-learning based particle
flow algorithm for CMS. The standard particle flow algorithm reconstructs
stable particles based on calorimeter clusters and tracks to provide a global
event reconstruction that exploits the combined information of multiple
detector subsystems, leading to strong improvements for quantities such as jets
and missing transverse energy. We have studied a possible evolution of particle
flow towards heterogeneous computing platforms such as GPUs using a graph
neural network. The machine-learned PF model reconstructs particle candidates
based on the full list of tracks and calorimeter clusters in the event. For
validation, we determine the physics performance directly in the CMS software
framework when the proposed algorithm is interfaced with the offline
reconstruction of jets and missing transverse energy. We also report the
computational performance of the algorithm, which scales approximately linearly
in runtime and memory usage with the input size.
