[pythran] Re: Compiler troubles on diffusion demo

  • From: serge Guelton <serge.guelton@xxxxxxxxxxxxxxxx>
  • To: pythran@xxxxxxxxxxxxx
  • Date: Wed, 9 Apr 2014 21:19:38 +0200

Hi again,

On Wed, Apr 09, 2014 at 06:05:54PM +0100, Ian Ozsvald wrote:
> 
> You make this look too easy :-) The bug-fix works and my code with
> both methods (your original demo and a chunked version) produce the
> same output. Thanks. Now for the next problem...

That's the problem with users, they always run into bugs :-)

> What is the behaviour of garbage collection with pythran? I have a
> pythran function that creates a new array (np.empty) on each call. If
> I run this without pythran (pure python+numpy) then I don't see a
> build-up of memory, Python's garbage collector sees it goes out of
> scope. When I run it with Pythran I rapidly fill 8GB as the temp array
> seems to be retained. What should I be aware of? Is a reference to the
> temp item kept alive somehow?

That's strange. I've tried this one:

    #pythran export mem(int)
    import numpy as np
    def mem(n):
        return np.empty(n)

Then ran a loop
    for i in range(1000000):
        mem.mem(1000000)

And I see no memory leaks.

The only situation where we leak is when we get a numpy array from
Python, as in

    #pythran export mem(int[])
    def mem(arr):
        return arr # we do a very conservatif reference increment here

What's your full test case?

> 
> With python+numpy only I use:
> def evolve(grid, dt):
>     D = 1
>     lap = laplacian(grid)
>     new_grid = np.empty(grid.shape)
> but due to the memory build-up I have to use:
> def evolve(grid, dt, new_grid):  # new_grid passed in from python routine
>     D = 1
>     lap = laplacian(grid)
> 
> Now I'm trying to use openmp - it runs (I can see 8 threads running at
> 11% CPU each in htop) and memory usage is stable, but the original
> (non-parallel) and parallel versions take the same execution time.
> Both versions take the new_grid argument (rather than make a temporary

I need more time to investigate this one. my guess is that Pythran's
reference couting is issuing memory barrier that prevent //ion, but
that's just a guess.


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