Strategies for profiling functions in ipyparallel

I have a function that is going slow and I would like to figure out what is holding it up. I have profiled the underlying non-parallel function and it goes about as fast as it can on data of a size which it can handle. However, it really needs to work on blocks of data in parallel to get done in a reasonable amount of time and to work on data of any significance. So, I really need to profile it while it is running in parallel with `ipyparallel`. Are there any suggested ways to go about this? Is it possible to use some existing tool like `line_profiler`? If not, what other tools might be look at? Does `ipyparallel` have any tricks for doing this sort of thing?

The issue names ipyparallel, line_profiler, and parallel worker functions but identifies no files, tests, or entry points. Review the existing profiling and parallel-execution guidance, determine whether line_profiler or another tool supports this workflow, and document a reproducible recommendation for profiling functions while they run in parallel.

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