On 10/14/2015 01:23 AM, Nadav Horesh wrote:
>
> I have binary files of size range between few MB to 1GB, which I read process
> as memory mapped files (via np.memmap). Until numpy 1.9 the creation of
> recarray on an existing file (without reading its content) was instantaneous,
> and now it takes ~6 seconds (system: archlinux on sandy bridge). A profiling
> (using ipython %prun) top of the list is:
>
>
> ncalls tottime percall cumtime percall filename:lineno(function)
> 21 3.037 0.145 4.266 0.203
> _internal.py:372(_check_field_overlap)
> 3713431 1.663 0.000 1.663 0.000 _internal.py:366(<genexpr>)
> 3713750 0.790 0.000 0.790 0.000 {range}
> 3713709 0.406 0.000 0.406 0.000 {method 'update' of 'set'
> objects}
> 322 0.320 0.001 1.984 0.006 {method 'extend' of 'list'
> objects}
>
> Nadav.
Hi Nadav,
The slowdown is due to a problem in PR I introduced to add safety checks
to views of structured arrays (to prevent segfaults involving object
fields), which will hopefully be fixed quickly. It is being discussed here
https://github.com/numpy/numpy/issues/6467
Also, I do not think the problem is with memmap - as far as I have
tested, memmmap is still fast. Most likely what is slowing your script
down is subsequent access to the fields of the array, which is what has
regressed. Is that right?
Allan
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