I cProfiled old versus your last commit. The graph has 7 953 158 vertices, I sample 100 vertices and do shortest_distance from each one. I specify no target, and set the max_dist parameter. After each call to shortest_distance, I use the reached array to reset the predecessor map. In average each search reaches 120177 vertices.
- 2.22 : 9.101 seconds
- commit 26404ef4 : 4.536 seconds
- commit 26404ef4 + no dist map init : 4.141 seconds
That more than twice faster, great news !
About the third configuration (no dist map init), I removed the distance map initialization (
graph_tool/topology/__init__.py L1779) and used the reached array to reset dist_map to infinity.
We could have a do_initialization parameter, I think it would be more explicit, see
my proposal.
Le ven. 30 juin 2017 à 21:59, Tiago de Paula Peixoto <
tiago@skewed.de> a écrit :
On 30.06.2017 18:44, François Kawala wrote:
> I realize that I'm not familiar enough with boost to do this change.
>
> From what I get, I'll add three private members : _distance and _predecessor
> they would be initialized as follows :
>
> _distance(get(vertex_distance, *_mg)),
> _predecessor(get(predecessor, *_mg)),
>
> I don't know if the _distance member should be filled with zeros ?
>
> The last private member would be _reach a std::vector<size_t>
>
> From that I'll declare two functions :
>
> set_reached to update the reached vertices
> reset_distance to reset the _distance, _predecessor and _reach to their
> default values.
>
> Does that sounds right ? If so, I'll guess that I'll have to make
> the do_djk_search function to call the set_reached and reset_distance functions.
>
> Am I missing something ?
>
> Sorry for this stuttering approach.
I've just pushed a commit that implements what you want; you can look inside
for the details.
In short, you can now do multiple searches as follows:
dist_map = g.new_vp("double", numpy.inf) # must be infinity
pred_map = g.vertex_index.copy() # must be identity
for source in sources:
# the following does not initialize dist_map and pred_map, and
# returns an array of reached vertices
dist_map, pred_map, reached = \
shortest_distance(g, source, weights=w, pred_map=pred_map,
dist_map=dist_map, return_reached=True)
# reset property maps for next search; this is O(len(reached))
dist_map.a[reached] = numpy.inf
pred_map.a[reached] = reached
Please tell me if this brings the improvements you were seeking.
Best,
Tiago
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