[firedrake] leaking memory

[David Ham]

I agree about the hard limit. There is a limit to how much useful
reordering we are ever going to do on the trace. Shall we set a default
value for lazy_max_trace_length? Is 100 a good number?
On Wed, 29 Apr 2015 at 10:00 Mitchell, Lawrence <
lawrence.mitchell at imperial.ac.uk> wrote:

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> On 29/04/15 08:59, Hiroe Yamazaki wrote:
> > Dear all,
> >
> > I am experiencing a memory leak in our vertical slice model and
> > wonder if there is any possibility of leaking memory associated
> > with .assign(). Here I have a stand-alone code with the same
> > function spaces and variable settings we use in the slice model:
> >
> >
> https://bitbucket.org/hyamazak/test_files/src/f455e7e68722c9264845a8426ba91cc040d3f933/test_memory.py?at=master
> >
> >  What I do here is just doing
> >
> > new.vars += Deltavars old.vars.assign(new.vars)
> >
> > over and over again, and I see a constant increase of the memory
> > usage both on my Linux and Macbook. I believe new.vars += Deltavars
> > is not the source of leaking and wonder if there is any problem
> > with doing old.vars.assign(new.vars).
> >
> > Do let me know if you have any ideas,
>
> OK, I observe the same memory leak.  Here's what's going on:
>
> PyOP2 is running in lazy evaluation mode, which means that no
> computation actually occurs until you ask to look at the resulting
> data (perhaps by writing it to a file, or dropping into a solve).
> Instead, we store an "execution trace" which remembers the sequence of
> computations you want to do.  By default this execution trace is
> unbounded in length, so, since you never look at the answer in the
> above code, we end up creating a trace of length 100000.  This is the
> cause of the memory leak.
>
> Arguably we should have a limit on the trace length.  Can you try
> setting the following parameter to see if your leak goes away?
>
> parameters["pyop2_options"]["lazy_max_trace_length"] = 100
>
>
> Cheers,
>
> Lawrence
>
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