Thanks for these clarifications.

I guess that most of the time is spent in the copy_property [link to git]. Indeed, when I provide a pre-initialized distance vector through the dist_map parameter, the execution of shortest_distance is only 10ms faster.

If I understood well, the copy_property is used to build a vector initialized with one single value. If so, one would obtain the same result with python and numpy with

x = np.empty(array_size, dtype=float)
x.fill(value)

I did try to time this "numpy way initialization" (altough I'm not sure it corresponds to copy_property)

python -m timeit -s "import numpy as np" -n 100 "np.empty(33e6, dtype=int).fill(1)"
100 loops, best of 3: 34.9 msec per loop

These 34.9ms have to be compared to the 300ms (bake of envelope calculus) that takes the copy_property function.

Am I right about the way that the copy_property function works ? Could it be improved ?

Best,
F.

2015-03-08 20:11 GMT+01:00 Tiago Peixoto [via Main discussion list for the graph-tool project] :
On 07.03.2015 18:20, François wrote:

> Consider two graphs: G=(V,E) and H=(V',E') such that:
>
>  1. V' \subset V and E' \subset E
>  2. |E| = 66M
>  3. |E'|=2M.
>
> Run:
>
>  1. X = shortest_distance(G, G.vertex(0), max_dist=x, weights=G.ep['weights'])
>  2. Y = shortest_distance(H, H.vertex(0), max_dist=x, weights=H.ep['weights'])
>
> I verify that : X = Y
>
> The execution took 320ms for (1) and 16ms for (2). However the number
> of visited vertices should be identical ? I expected to obtain
> comparable execution times.
This is because of the initialization of the algorithm. The algorithm
needs to create and initialize at least two vectors of size |V| to store
the distances and book-keeping, regardless of how many vertices are in
fact visited. Therefore the algorithm will never be faster than O(|V|),
independently of "max_dist".

Here is more or less what you are describing:

In : g = lattice([10, 10])

In : %time d = shortest_distance(g, g.vertex(0), max_dist=2)
CPU times: user 3.33 ms, sys: 0 ns, total: 3.33 ms
Wall time: 2.43 ms

In : g = lattice([1000, 1000])

In : %time d = shortest_distance(g, g.vertex(0), max_dist=2)
CPU times: user 50 ms, sys: 0 ns, total: 50 ms
Wall time: 28.6 ms

Note that the second graph is ten thousand times larger than the first
one, but the algorithm was only a little more than ten times slower.

Now if we do it for the larger graph without max dist, we have:

In : %time d = shortest_distance(g, g.vertex(0))
CPU times: user 270 ms, sys: 0 ns, total: 270 ms
Wall time: 260 ms

Which is around 10 times slower... So thing are indeed faster if
max_dist is specified, but the algorithm will still depend on the total
size of the graph.

Best,
Tiago

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