  In shared-memory parallel automatic differentiation, shared inputs among
simultaneous thread-local preaccumulations lead to data races if Jacobians are
accumulated with a single, shared vector of adjoint variables. In this work, we
discuss the benefits and tradeoffs of re-enabling such preaccumulations by a
transition to suitable local adjoint variables. In particular, we assess the
performance of mapped local adjoints in discrete adjoint computations in the
multiphysics simulation suite SU2.
