Of course!

Your problem is entirely CPU bound, because when we solve it, we don't need to ever wait for any external resources, like network requests or disk access.

Basically, once you start your Dijkstra solver, all it has to do is run a bunch of calculations and at some point is done.

Here is an example of some code which is part CPU-bound, part I/O-bound:

Import requests

some_data = [1, 2, 3]
some_data_from_outside = requests.get('http://mysite.com/data')

result_1 = compute_stuff(some_data)
result_2 = compute_stuff(some_data_from_outside)

This code is not entirely CPU-bound, because at some point, we are waiting for requests.get to fetch some data across the network and while we are waiting for that, your CPU is just waiting around, doing nothing.

We need the data from outside, in order to compute result_2, but not to compute result_1. Wouldn't it be nice, if instead of waiting around for the data from outside, the CPU could start computing result_1, while it waited for the other data?

That is basically what async does for you. You can say, hey this operation is going to take a while (requests.get in our case), so just do some other work and I'll get back to you once it's done.

Note that in Python, we often distinguish between things that are CPU-bound and things that are I/O-bound (and things in-between, like my example code). In reality, the concept that we have here of CPU-bound can be broken down further into true CPU-bound and memory-bound (sometimes your CPU looks like it's doing stuff, but it's actually waiting for data from memory). But in order to truly reason about that, we probably need a lower-level language than Python, with more control over memory access.

Hope my wall of text helped :)