Thanks for these clarifications.
I guess that most of the time is spent in the *copy_property* [link to git
<https://git.skewed.de/count0/graph-tool/blob/master/src/graph/graph_properties_copy.cc#L33>].
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] <[email protected]>:
> 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 [1]: g = lattice([10, 10])
>
> In [2]: %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 [3]: g = lattice([1000, 1000])
>
> In [4]: %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 [5]: %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
>
> --
> Tiago de Paula Peixoto <[hidden email]
> <http:///user/SendEmail.jtp?type=node&node=4026027&i=0>>
>
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