  Dedispersion, the removal of deleterious smearing of impulsive signals by the
interstellar matter, is one of the most intensive processing steps in any radio
survey for pulsars and fast transients. We here present a study of the
parallelization of this algorithm on many-core accelerators, including GPUs
from AMD and NVIDIA, and the Intel Xeon Phi. We find that dedispersion is
inherently memory-bound. Even in a perfect scenario, hardware limitations keep
the arithmetic intensity low, thus limiting performance. We next exploit
auto-tuning to adapt dedispersion to different accelerators, observations, and
even telescopes. We demonstrate that the optimal settings differ between
observational setups, and that auto-tuning significantly improves performance.
This impacts time-domain surveys from Apertif to SKA.
