On 09.12.2010, at 00:25, Frank Schima wrote:
> On Dec 8, 2010, at 9:28 AM, Eric A. Borisch wrote:
>
>> == Executive summary ==
>> The OS python has different numpy exception handling settings, and this is
>> the reason for the performance difference.
...
>> I haven't looked at how or why these s
On Dec 8, 2010, at 9:28 AM, Eric A. Borisch wrote:
> == Executive summary ==
> The OS python has different numpy exception handling settings, and this is
> the reason for the performance difference.
>
> == The brief results ==
> with numpy.seterr(all='ignore') -> 13s system, 12s macports
> with
== Executive summary ==
The OS python has different numpy exception handling settings, and this is
the reason for the performance difference.
== The brief results ==
with numpy.seterr(all='ignore') -> 13s system, 12s macports
with numpy.seterr(all='print') and numpy.seterr(under='ignore') -> 56s
s
On 08.12.2010, at 08:21, Konrad Hinsen wrote:
> Update: I profiled both runs using Shark, and found that when using the
> MacPorts version, 57% of the total time is spent in function feclearexcept.
> In the MacPython run, this function doesn't even show up. This makes me
> suspect that the diff
I don't think it is a specifically Macports issue. On my (oldish)
Macbook pro the system python & numpy with
/usr/bin/python bench2.py takes 15 s
while the 32/64 bit numpy I built took 52 s
No difference between 32 and 64 bit (i.e.python-32 instead of python-64)
Same ratio on my Mac Pro: 2
On 07.12.2010, at 21:58, Konrad Hinsen wrote:
> Somewhat by accident I noticed an enormous speed difference in basic NumPy
> operations between my MacPorts installation (py26-numpy) and the NumPy 1.5.1
> binaries from the NumPy sourceforge site used with MacPython 2.6, also
> downloaded as a bi
On Tue, Dec 7, 2010 at 23:02, Konrad Hinsen wrote:
> My installation uses the default, gcc 4.4, and my numbers are for a MacBook
> Pro running at 2.53 GHz.
>
> Does gcc-4.2 mean you used the gcc from Apple's Xcode package?
Even though you are using the gcc44 variant only the fortran compiler
fr
On Dec 7, 2010, at 10:02 PM, Konrad Hinsen wrote:
> On 08.12.2010, at 00:31, Frank Schima wrote:
>
>> Wow, interesting result. I just ran the benchmark on my Mac Pro 2008 and got
>> 44 s with py26-numpy. I rebuilt it just to take a look. I noticed it is
>> using -O3 in the configure flags which
On 08.12.2010, at 00:31, Frank Schima wrote:
> Wow, interesting result. I just ran the benchmark on my Mac Pro 2008 and got
> 44 s with py26-numpy. I rebuilt it just to take a look. I noticed it is using
> -O3 in the configure flags which is good, but it was using gcc-4.2 to build.
> I have the
On Tue, Dec 7, 2010 at 17:31, Frank Schima wrote:
> Wow, interesting result. I just ran the benchmark on my Mac Pro 2008 and got
> 44 s with py26-numpy. I rebuilt it just to take a look. I noticed it is using
> -O3 in the configure flags which is good, but it was using gcc-4.2 to build.
> I ha
In article ,
Konrad Hinsen wrote:
> Somewhat by accident I noticed an enormous speed difference in basic NumPy
> operations between my MacPorts installation (py26-numpy) and the NumPy 1.5.1
> binaries from the NumPy sourceforge site used with MacPython 2.6, also
> downloaded as a binary.
>
>
On Dec 7, 2010, at 1:58 PM, Konrad Hinsen wrote:
> Somewhat by accident I noticed an enormous speed difference in basic NumPy
> operations between my MacPorts installation (py26-numpy) and the NumPy 1.5.1
> binaries from the NumPy sourceforge site used with MacPython 2.6, also
> downloaded as
Somewhat by accident I noticed an enormous speed difference in basic NumPy
operations between my MacPorts installation (py26-numpy) and the NumPy 1.5.1
binaries from the NumPy sourceforge site used with MacPython 2.6, also
downloaded as a binary.
~/projects/solar_system> /usr/local/bin/python b
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