> You can grab one of those objects on the heap and look at its backreference
> graph
How does one do that?
- Marc
On Sun, Sep 15, 2024 at 11:17 AM Nils Bruin <nbr...@sfu.ca> wrote:
>
> On Sunday 15 September 2024 at 10:08:18 UTC-7 dim...@gmail.com wrote:
>
> For me this code is rather unpredictable, as ipython and prompt_toolkit kick
> in
> and produce extra objects.
>
> For consistency (at least them the output values are reproducible)
> it looks better to experiment with Sage's python (./sage --python)
> for which the code needs to be adjusted, with "^" replaced by "**", and
> two lines added as the front:
>
> from sage.all import *
> var('x')
>
>
> Generally, to check if there's a leak one would look how object swell on the
> heap grows with iterations. That drowns out the constant overhead of the
> shell.
> Or you can just wrap the whole measuring process in a function, so that it
> gets executed in an enclosed scope. There are generally some objects whose
> creation does scale with iteration and whose type is rather specific. You can
> grab one of those objects on the heap and look at its backreference graph.
> That generally gives a pretty good pointer to where the global references are
> held. It can actually be a fairly interesting challenge for someone with an
> interest in learning the nitty gritty details of the sagemath memory setup.
> It doesn't require brilliance; mostly persistence.
>
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