Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Find and resolve bottlenecks #53

Open
jGaboardi opened this issue Dec 17, 2020 · 1 comment
Open

Find and resolve bottlenecks #53

jGaboardi opened this issue Dec 17, 2020 · 1 comment
Assignees

Comments

@jGaboardi
Copy link
Owner

jGaboardi commented Dec 17, 2020

BY far the two largest time/memory hogs are [n2n_matrix] and [paths] within Network.cost_matrix():

# calculate shortest path length and records paths if desired
n2n_matrix, paths = utils.shortest_path(self, gp=wpaths)

An attempt to speed up should be made...

  • try numba?
  • simple multiprocessing?
@jGaboardi
Copy link
Owner Author

Try:

  • multiprocessing for observations
  • numba

@jGaboardi jGaboardi changed the title multiprocessing for cost matrices? Find and resolve bottlenecks Dec 23, 2020
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

1 participant