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While many ideas and proofs of concept for parallel-in-time integrationmethods exists, the number of large-scale, accessible time-parallel codes israther small. This is often due to the apparent or subtle complexity of thealgorithms and the many pitfalls awaiting developers of parallel numericalsoftware. One example of such a time-parallel code is pySDC, which implements,among others, the parallel full approximation scheme in space and time(PFASST). Inspired by nonlinear multigrid ideas, PFASST allows to integratemultiple time-steps simultaneously using a space-time hierarchy of spectraldeferred corrections. In this paper we demonstrate the application ofperformance analysis tools to the PFASST implementation pySDC. Tracing the pathwe took for this work, we highlight the obstacles encountered, describeremedies and explain the sometimes surprising findings made possible by thetools. Although focusing only on a single implementation of a particularparallel-in-time integrator, we hope that our results and in particular the waywe obtained them are a blueprint for other time-parallel codes.

Using performance analysis tools for parallel-in-time integrators -- Does my time-parallel code do what I think it does?