  The precise mechanisms responsible for the natural dynamos in the Earth and
Sun are still not fully understood. Numerical simulations of natural dynamos
are extremely computationally intensive, and are carried out in parameter
regimes many orders of magnitude away from real conditions. Parallelization in
space is a common strategy to speed up simulations on high performance
computers, but eventually hits a scaling limit. Additional directions of
parallelization are desirable to utilise the high number of processor cores now
available. Parallel-in-time methods can deliver speed up in addition to that
offered by spatial partitioning but have not yet been applied to dynamo
simulations. This paper investigates the feasibility of using the
parallel-in-time algorithm Parareal to speed up initial value problem
simulations of the kinematic dynamo, using the open source Dedalus spectral
solver. Both the time independent Roberts and time dependent Galloway-Proctor
2.5D dynamos are investigated over a range of magnetic Reynolds numbers.
  Speed ups beyond those possible from spatial parallelization are found in
both cases. Results for the Galloway-Proctor flow are promising, with Parareal
efficiency found to be close to 0.3. Roberts flow results are less efficient,
but Parareal still shows some speed up over spatial parallelization alone.
  Parallel in space and time speed ups of $\sim300$ were found for 1600 cores
for the Galloway-Proctor flow, with total parallel efficiency of $\sim0.16$.
