  With the High Luminosity LHC coming online in the near future, event
generators will need to provide very large event samples to match the
experimental precision. Currently, the estimated cost to generate these events
exceeds the computing budget of the LHC experiments. To address these issues,
the computing efficiency of event generators need to be improved. Many
different approaches are being taken to achieve this goal. I will cover the
ongoing work on implementing event generators on the GPUs, machine learning the
matrix element, machine learning the phase space, and minimizing the number of
negative weight events.
