[Numpy-discussion] Numpy x Matlab: some synthetic benchmarks

[Travis Oliphant]

Travis Oliphant wrote:

>
> This is what I found:  overall
>
>   61242 53.9606 /usr/bin/python
>    17647 15.5488 
> /usr/lib/python2.4/site-packages/numpy/core/multiarray.so
>    15942 14.0466 /lib/tls/libc-2.3.3.so
>     7158  6.3069 /no-vmlinux
>     6995  6.1633 /usr/lib/python2.4/site-packages/Numeric/_numpy.so
>
> Showing that more time is spent in NumPy than in Numeric doing 
> indexing...
>
I optimized PyArray_PyIntAsIntp and changed things so that extra 
allocations are not done if the indexing is not fancy in the new svn of 
numpy and improved the performance of indexing a bit...

The new numbers...

samples  %        symbol name
551      18.4775  slice_GetIndices
482      16.1636  array_subscript
343      11.5023  parse_index
309      10.3622  PyArray_PyIntAsIntp
166       5.5667  fancy_indexing_check
164       5.4997  _IsAligned
140       4.6948  array_dealloc
134       4.4936  parse_subindex
133       4.4601  anonymous symbol from section .plt
127       4.2589  PyArray_NewFromDescr
121       4.0577  slice_coerce_index
79        2.6492  _IsFortranContiguous
67        2.2468  array_subscript_nice
56        1.8779  _IsContiguous
49        1.6432  array_alloc
31        1.0396  PyArray_UpdateFlags
29        0.9725  PyArray_Return
1         0.0335  array_itemsize_get


For reference, the Numeric numbers are:

samples  %        symbol name
375      25.9695  slice_GetIndices
255      17.6593  parse_index
198      13.7119  PyArray_IntegerAsInt
135       9.3490  array_subscript
130       9.0028  PyArray_FromDimsAndDataAndDescr
117       8.1025  parse_subindex
81        5.6094  slice_coerce_index
55        3.8089  array_subscript_nice
44        3.0471  array_dealloc
40        2.7701  anonymous symbol from section .plt
13        0.9003  PyArray_Return
1         0.0693  do_sliced_copy


These look a bit better and the results show that simple indexing seems 
to be only slowed down by the fancy indexing check...

-Travis