Am 04.03.21 um 16:53 schrieb ignacioRh:
Hey,
Thanks a lot for the program. I'm using it on an academic project which would have died long ago without it.
I'm now running some large simulations and need to speed up how I use the shortest_distance, which is in a python for loop
My use case is the following: I have a large graph, and I need to compute the shortest distance between an origin subset of the nodes [A,B], to a target subset [X,Y]. So I want the distances AX, AY, BX, BY. It is infeasible to compute all pairs distances on the graph, its too large
I can pass a list to shortest_distance as a target, but I can't pass a list as an origin. Hence I use list comprehension to achieve the result distances = [shortest_distances(origin, target= [X,Y] for origin in [A,B]]
This is the main bottleneck in my code.
I don't understand why this would be. If your network is very large, surely most of the time is spent in the inner loop, rather than the outer one.
I'm wondering if I can somehow put the for loop for the origins in some type of cpython extension, would that result in a speed up gain? So if put the underlying libgraph_tool_topology.get_dists() call into some cython-type code, do you think that would speed up this for loop?
If the bottleneck is really there, this could help, but not by much. But again, I think it's unlikely, unless your graph is quite small. Best, Tiago -- Tiago de Paula Peixoto <tiago@skewed.de>