On Friday July 11 2008, Don Stewart wrote:
> Do you have the bencmark code? I'd like to try a couple of variants on
> the underlying structures.
It's not a thorough test but I suppose it gives an impression about
performance.
-- Gokhan
$ ghc -O -prof --make TestGraph
$ ./TestGraph +RTS -s -P -R
gsan:
> On Friday July 11 2008, Andre Nathan wrote:
> > On Thu, 2008-07-10 at 16:52 -0700, Don Stewart wrote:
> > > Well, they're radically different graph representations, and fgl
> > > hasn't been designed for large graphs.
> >
> > Do you know if King and Launchbury's implementation (Data.Graph)
On Friday July 11 2008, Andre Nathan wrote:
> On Thu, 2008-07-10 at 16:52 -0700, Don Stewart wrote:
> > Well, they're radically different graph representations, and fgl
> > hasn't been designed for large graphs.
>
> Do you know if King and Launchbury's implementation (Data.Graph) scales
> better?
On Thu, 2008-07-10 at 16:52 -0700, Don Stewart wrote:
> Well, they're radically different graph representations, and fgl
> hasn't been designed for large graphs.
Do you know if King and Launchbury's implementation (Data.Graph) scales
better?
> What C library is Ruby's binding to? It might be quit
andre:
> On Thu, 2008-07-10 at 18:32 -0400, Ronald Guida wrote:
> > Your ratios are about 1 : 3 : 8.
> > That pretty close to quadratic growth, 1 : 4 : 9, so I think all is well.
>
> Maybe, but 96MB of resident memory for a 1000-node graph looks bad,
> especially considering p is low. Is the inter
On Thu, 2008-07-10 at 18:32 -0400, Ronald Guida wrote:
> Your ratios are about 1 : 3 : 8.
> That pretty close to quadratic growth, 1 : 4 : 9, so I think all is well.
Maybe, but 96MB of resident memory for a 1000-node graph looks bad,
especially considering p is low. Is the internal representation
On Thu, Jul 10, 2008 at 4:57 PM, Andre Nathan <[EMAIL PROTECTED]> wrote:
> Hello
>
> I'm trying to create a directed graph using the Data.Graph.Inductive.
> The graph is a random graph using the G(n, p) model, that is, each of
> the n nodes is linked to every other node with probability p.
So the
Hello
I'm trying to create a directed graph using the Data.Graph.Inductive.
The graph is a random graph using the G(n, p) model, that is, each of
the n nodes is linked to every other node with probability p.
I'm seeing a large increase of memory usage when n grows (this is using
p = 0.1):
n = 10
