  In recent years, various means of efficiently detecting changepoints in the univariate setting have been proposed, with one popular approach involving minimising a penalised cost function using dynamic programming. In some situations, these algorithms can have an expected computational cost that is linear in the number of data points; however, the worst case cost remains quadratic. We introduce two means of improving the computational performance of these methods, both based on parallelising the dynamic programming approach. We establish that parallelisation can give substantial computational improvements: in some situations the computational cost decreases roughly quadratically in the number of cores used. These parallel implementations are no longer guaranteed to find the true minimum of the penalised cost; however, we show that they retain the same asymptotic guarantees in terms of their accuracy in estimating the number and location of the changes. 
Parallelisation of a Common Changepoint Detection Method