Type instabilities when introducing a Channel as a parameter in a ODE problem

General Usage Performance
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1

Hello.

Hello, in the following I show a minimal working example of the problem. I want to implement a type stable function that solves an ODE that implements a Channel as a parameter. But I figured out that the solve function returns Any type.

This is the minimal code:

function prova(A, b, tspan; alg=Tsit5())
    progr_channel = Channel{Bool}()
    p = (L=A,progr_channel=progr_channel)
    prob = ODEProblem{true}(prova_func, b, tspan, p)
    sol = solve(prob, alg)
    return prova(sol)
end

function prova(sol)
    return sol
end

prova_func(du, u, p, t) = mul!(du, p.L, u)

A = rand(5,5)
b = rand(5)
tspan = (0.0, 1.0)
res = prova(A, b, tspan)

This channel will be helpful in the future for implementing a ProgressMeter in an ensemble problem or even a simple ode (if I want more customizations)

@async while take!(progr_channel)
    next!(myProgressMeter)
end

# and in a function
pop!(prob.p.progr_channel)

but when I use Cthulhu to discover type instabilities, I see that the function solve returns Any. After that, it is impossible for julia to identify the type of the outcome of the solution.

How to solve it?

1 Like
2

This is worth an issue.

3

A bit of a tangent but there is series of PRs being actively work on right now for this feature, which you might be interest in.

I think they are getting close to done.

4

Thanks for telling me this the existence of this PR. However, the use of an external Progress Meter might be useful in a multitude of situations.

For example, we can redirect the stderr output to a file and print the progress bar on that file, which is very useful when using remote machines with very long tasks, and I don’t think it is supported by the native progress bar.

Moreover, the current progress meter inside the ODE is not much supported in jupyter notebook. I tested it once and it printed line by line the progress bar, which is very confusing.

And last but not least, it can be easily implemented to show progress bars of ensemble problems.

By the way, this topic goes beyond the use of a progress meter, since the Channel (or even a Remote Channel) might be used in a multitude of ways.

5

But you can always just use a function barrier on the solve so if your ODE solve is >100ns it’s a non-issue in practice.

But as a way to handle this, try overloading DiffEqBase.anytypedual(x::Channel, counter = 0) = Any.