I knew something was not right. I typed qr, not lu. Hence in that case,
MATLAB did pivoting and Julia didn't. Sorry for that.
Here are the right timings for lu which are as expected. MKL is slightly
faster than OpenBLAS.
MATLAB:
>> tic();for i = 1:10
[L,U,p] = lu(A, 'vector');
end;toc()/10
ans =
0.2314
Julia:
julia> tic(); for i = 1:10
lu(A);
end;toc()/10
elapsed time: 3.147632455 seconds
0.3147632455
Med venlig hilsen
Andreas Noack
2014-09-18 16:25 GMT-04:00 Stefan Karpinski <ste...@karpinski.org>:
> Nice :-)
>
> On Thu, Sep 18, 2014 at 4:20 PM, Andreas Noack <
> andreasnoackjen...@gmail.com> wrote:
>
>> Yes. It appears so on my Mac. I just redid the timings with the same
>> result.
>>
>> Med venlig hilsen
>>
>> Andreas Noack
>>
>> 2014-09-18 15:55 GMT-04:00 Stefan Karpinski <ste...@karpinski.org>:
>>
>> I'm slightly confused – does that mean Julia is 2.4x faster in this case?
>>>
>>> On Thu, Sep 18, 2014 at 3:53 PM, Andreas Noack <
>>> andreasnoackjen...@gmail.com> wrote:
>>>
>>>> In addition our lu calculates a partially pivoted lu and returns the L
>>>> and U matrices and the vector of permutations. To get something comparable
>>>> in MATLAB you'll have to write
>>>>
>>>> [L,,U,p] = lu(A,'vector')
>>>>
>>>> On my old Mac where Julia is compiled with OpenBLAS the timings are
>>>>
>>>> MATLAB:
>>>> >> tic();for i = 1:10
>>>> [L,U,p] = qr(A, 'vector');
>>>> end;toc()/10
>>>>
>>>> ans =
>>>>
>>>> 3.4801
>>>>
>>>> Julia:
>>>> julia> tic(); for i = 1:10
>>>> qr(A);
>>>> end;toc()/10
>>>> elapsed time: 14.758491472 seconds
>>>> 1.4758491472
>>>>
>>>> Med venlig hilsen
>>>>
>>>> Andreas Noack
>>>>
>>>> 2014-09-18 15:33 GMT-04:00 Jason Riedy <ja...@lovesgoodfood.com>:
>>>>
>>>> And Elliot Saba writes:
>>>>> > The first thing you should do is run your code once to warm up the
>>>>> > JIT, and then run it again to measure the actual run time, rather
>>>>> > than compile time + run time.
>>>>>
>>>>> To be fair, he seems to be timing MATLAB in the same way, so he's
>>>>> comparing systems appropriately at that level.
>>>>>
>>>>> It's just the tuned BLAS+LAPACK & fftw v. the default ones. This
>>>>> is one reason why MATLAB bundles so much. (Another reason being
>>>>> the differences in numerical results causing support calls. Took
>>>>> a long time before MATLAB gave in to per-platform-tuned libraries.)
>>>>>
>>>>>
>>>>
>>>
>>
>
