What sorts of functions take advantage of MKL?
Linear Algebra (equation solving)?
Something like dot product?
exp, log, trig of matrix?
basic numpy arithmetic? (add matrixes)
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Hi,
I think you have at least linear algebra (lapack) and dot. Basic
arithmetics will not benefit, for expm, logm... I don't know.
Matthieu
2013/4/19 Neal Becker ndbeck...@gmail.com
What sorts of functions take advantage of MKL?
Linear Algebra (equation solving)?
Something like dot
KACVINSKY Tom wrote:
You also get highly optimized BLAS routines, like dgemm and degemv.
And does numpy/scipy just then automatically use them? When I do a matrix
multiply, for example?
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For the matrix multiplication or array dot, you use BLAS3 functions as they
are more or less the same. For the rest, nothing inside Numpy uses BLAS or
LAPACK explicitelly IIRC. You have to do the calls yourself.
2013/4/19 Neal Becker ndbeck...@gmail.com
KACVINSKY Tom wrote:
You also get
On Apr 18, 2013, at 11:33 PM, Nathaniel Smith n...@pobox.com wrote:
On 18 Apr 2013 01:29, Chris Barker - NOAA Federal chris.bar...@noaa.gov
wrote:
This has been annoying, particular as rank-zero scalars are kind of a
pain.
BTW, while we're on the topic, can you elaborate on this? I tend to
Looks like the *lapack_lite files have internal calls to dgemm. I alos found
this:
http://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
So it looks like numpy/scipy performs better with MKL, regardless of how the
MKL routines are called (directly, or via a numpy/scipy
On Sun, Apr 7, 2013 at 2:09 AM, Ondřej Čertík ondrej.cer...@gmail.com wrote:
Hi,
I'm pleased to announce the availability of the final NumPy 1.7.1 release.
Sources and binary installers can be found at
https://sourceforge.net/projects/numpy/files/NumPy/1.7.1/
Only three simple bugs were
On Thu, Apr 18, 2013 at 10:04 PM, K.-Michael Aye kmichael@gmail.com wrote:
On 2013-04-19 01:02:59 +, Benjamin Root said:
So why is there an error in the 2nd case, but no error in the first
case? Is there a logic to it?
When you change a dtype like that in the first one, you aren't
The graph is a comparison of the dot calls, of course they are better with
MKL than the default BLAS version ;)
For the rest, Numpy doesn't benefit from MKL, scipy may if they call LAPACK
functions wrapped by Numpy or Scipy (I don't remember which does the
wrapping).
Matthieu
2013/4/19
Robert,
As I think you wrote the code, you may have a quick answer:
Given that numpy scalars do exist, and have their uses -- I found this
wiki page to remind me:
http://projects.scipy.org/numpy/wiki/ZeroRankArray
It would be nice if the .npy format could support them. Would that be
a major
On Fri, Apr 19, 2013 at 8:12 AM, Ondřej Čertík ondrej.cer...@gmail.com wrote:
I'm pleased to announce the availability of the final NumPy 1.7.1 release.
Nice work -- but darn! I was hoping a change/fix to teh datetime64
timezone handlien could get into the next release -- oh well.
When do we
On Fri, Apr 19, 2013 at 4:17 PM, Chris Barker - NOAA Federal
chris.bar...@noaa.gov wrote:
On Fri, Apr 19, 2013 at 8:12 AM, Ondřej Čertík ondrej.cer...@gmail.com
wrote:
I'm pleased to announce the availability of the final NumPy 1.7.1 release.
Nice work -- but darn! I was hoping a change/fix
Hi folks,
In [264]: np.__version__
Out[264]: '1.7.0'
I just noticed that deep copying a rank-zero array yields a scalar --
probably not what we want.
In [242]: a1 = np.array(3)
In [243]: type(a1), a1
Out[243]: (numpy.ndarray, array(3))
In [244]: a2 = copy.deepcopy(a1)
In [245]: type(a2), a2
On Fri, 2013-04-19 at 08:03 -0700, Chris Barker - NOAA Federal wrote:
On Apr 18, 2013, at 11:33 PM, Nathaniel Smith n...@pobox.com wrote:
On 18 Apr 2013 01:29, Chris Barker - NOAA Federal
chris.bar...@noaa.gov wrote:
This has been annoying, particular as rank-zero scalars are kind
On Fri, Apr 19, 2013 at 8:45 PM, Chris Barker - NOAA Federal
chris.bar...@noaa.gov wrote:
Robert,
As I think you wrote the code, you may have a quick answer:
Given that numpy scalars do exist, and have their uses -- I found this
wiki page to remind me:
On Fri, Apr 19, 2013 at 9:40 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Fun fact, array[()] will convert a 0-d array to a scalar, but do nothing
(or currently create a view) for other arrays. Which is actually a good
question. Should array[()] force a view or not?
Another fun fact:
On Fri, 2013-04-19 at 23:02 +0530, Robert Kern wrote:
On Fri, Apr 19, 2013 at 9:40 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Fun fact, array[()] will convert a 0-d array to a scalar, but do nothing
(or currently create a view) for other arrays. Which is actually a good
On Fri, Apr 19, 2013 at 8:46 AM, Nathaniel Smith n...@pobox.com wrote:
Nice work -- but darn! I was hoping a change/fix to teh datetime64
timezone handlien could get into the next release -- oh well.
That's probably too big a behavioural chance to go into a point
release in any case...
On Fri, Apr 19, 2013 at 10:21 AM, Robert Kern robert.k...@gmail.com wrote:
On Fri, Apr 19, 2013 at 8:45 PM, Chris Barker - NOAA Federal
chris.bar...@noaa.gov wrote:
Given that numpy scalars do exist, and have their uses -- I found this
wiki page to remind me:
On 19 Apr 2013 19:22, Chris Barker - NOAA Federal chris.bar...@noaa.gov
wrote:
Anyway -- going to HDF, or netcdf, or role-your-own really seems like
overkill for this. I just need something fast and simple and it
doesn't need to interchange with anything else.
Just use pickle...?
-n
On Fri, Apr 19, 2013 at 11:31 AM, Nathaniel Smith n...@pobox.com wrote:
On 19 Apr 2013 19:22, Chris Barker - NOAA Federal chris.bar...@noaa.gov
wrote:
Anyway -- going to HDF, or netcdf, or role-your-own really seems like
overkill for this. I just need something fast and simple and it
doesn't
19.04.2013 22:06, Chris Barker - NOAA Federal kirjoitti:
On Fri, Apr 19, 2013 at 11:31 AM, Nathaniel Smith n...@pobox.com wrote:
On 19 Apr 2013 19:22, Chris Barker - NOAA Federal chris.bar...@noaa.gov
wrote:
Anyway -- going to HDF, or netcdf, or role-your-own really seems like
overkill for
On Sat, Apr 20, 2013 at 12:36 AM, Chris Barker - NOAA Federal
chris.bar...@noaa.gov wrote:
On Fri, Apr 19, 2013 at 11:31 AM, Nathaniel Smith n...@pobox.com wrote:
On 19 Apr 2013 19:22, Chris Barker - NOAA Federal chris.bar...@noaa.gov
wrote:
Anyway -- going to HDF, or netcdf, or role-your-own
Hello everybody,
I just have one long string
type:ThesampletextthatcouldbereadedthesameinbothordersArozaupalanalapuazorA
The result I want to take is ArozaupalanalapuazorA - which means reading
directly each letter should be the same as reading reversely ...
Is there any function which can
One major advantage you can have using mkl is installing numexpr
compiling it with MLK.
That's a strong suggestion to easily use mkl and go faster on common
operations.
Xavier
On 20/04/2013 1:16 AM, Matthieu Brucher matthieu.bruc...@gmail.com
wrote:
The graph is a comparison of the dot calls,
Here's a seed for your function:
s = 'ThesampletextthatcouldbereadedthesameinbothordersArozaupalanalapuazorA'
f = np.array(list(s)).view('int8').astype(float)
f -= f.mean()
maybe_here = np.argmax(np.convolve(f,f))/2
magic = 10
print s[maybe_here - magic:maybe_here + magic + 1]
Let us now how to
Hi,
I am a bit unaware with that you put magic = 10 . Why?
Пятница, 19 апреля 2013, 19:05 -05:00 от Val Kalatsky kalat...@gmail.com:
Here's a seed for your function:
s = ' Thesampletextthatcouldbereaded thesameinbothordersArozaupalan alapuazorA
'
f =
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