Optimize memory usage in homogeneous_DUO_update via in-place update
#144
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This commit resolves a memory consumption issue in the `homogeneous_DUO_update` method by replacing the previous array reallocation with an in-place update using `np.place`. The old implementation created new NumPy arrays in each update cycle, leading to unnecessary memory overhead. With the in-place update, total memory usage was reduced from 1189 MB to 471 MB. Changes: - Switched to `np.place` for in-place updates of `s.route_pref`. - Removed unnecessary temporary array creation to reduce memory footprint.
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@toruseo The failure values look very small, could it be that just some rounding error and the tolerances are a bit tight? |
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The diff is not small. I guess (I dont fully understand the vectorizatoin technique used here) the modification broke the route choice logic. |
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Yes, took a look again, you’re right. I will try a few other approaches, and also investigate |
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Closing, as better one is implemented by #146 |
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This commit resolves a memory consumption issue in the
homogeneous_DUO_updatemethod by replacing the previous array reallocation with an in-place update usingnp.place. The old implementation created new NumPy arrays in each update cycle, leading to unnecessary memory overhead.With the in-place update, total memory usage was reduced from 1189 MB to 471 MB.
Changes
np.placefor in-place updates ofs.route_pref.Part of #143.