  Multitask learning and transfer learning have proven to be useful in the
field of machine learning when additional knowledge is available to help a
prediction task. We aim at deriving methods following these paradigms for use
in autotuning, where the goal is to find the optimal performance parameters of
an application treated as a black-box function. We show comparative results
with state-of-the-art autotuning techniques. For instance, we observe an
average $1.5x$ improvement of the application runtime compared to the OpenTuner
and HpBandSter autotuners. We explain how our approaches can be more suitable
than some state-of-the-art autotuners for the tuning of any application in
general and of expensive exascale applications in particular.
