  The ATLAS experiment at the Large Hadron Collider has a broad physics
programme ranging from precision measurements to direct searches for new
particles and new interactions, requiring ever larger and ever more accurate
datasets of simulated Monte Carlo events. Detector simulation with GEANT4 is
accurate but requires significant CPU resources. Over the past decade, ATLAS
has developed and utilized tools that replace the most CPU-intensive component
of the simulation -- the calorimeter shower simulation -- with faster
simulation methods. Here, AtlFast3, the next generation of high-accuracy fast
simulation in ATLAS is introduced. AtlFast3 combines parameterized approaches
with machine-learning techniques and is deployed to meet current and future
computing challenges and simulation needs of the ATLAS experiment. With highly
accurate performance and a new ability to model substructure within jets,
AtlFast3 is designed to be used to simulate large numbers of events for a wide
range of physics processes.
