Thanks everyone for helping me glimpse the secret world of FORTRAN
compilers. I am running a Linux machine, so I will look into MKL and
openBLAS. It was easy for me to get a Intel parallel studio XE license
as a student, so I have options.
___
NumPy-D
Hi,
I just ran both on the same hardware and got a slightly faster computation
with numpy:
Matlab R2012a: 16.78 s (best of 3)
numpy (python 3.4, numpy 1.10.1, anaconda accelerate (MKL)): 14.8 s (best
of 3)
The difference could because my Matlab version is a few years old, so it's
MKL would b
On Thu, Dec 17, 2015 at 5:52 AM, Sturla Molden
wrote:
> On 17/12/15 12:06, Francesc Alted wrote:
>
> Pretty good. I did not know that OpenBLAS was so close in performance
>> to MKL.
>>
>
> MKL, OpenBLAS and Accelerate are very close in performance, except for
> level-1 BLAS where Accelerate and
On 16/12/15 20:47, Derek Homeier wrote:
Getting around 30 s wall time here on a not so recent 4-core iMac, so that
would seem to fit
(iirc Accelerate should actually largely be using the same machine code as MKL).
Yes, the same kernels, but not the same threadpool. Accelerate uses the
GCD, M
On 17/12/15 12:06, Francesc Alted wrote:
Pretty good. I did not know that OpenBLAS was so close in performance
to MKL.
MKL, OpenBLAS and Accelerate are very close in performance, except for
level-1 BLAS where Accelerate and MKL are better than OpenBLAS.
MKL requires the number of threads t
2015-12-17 12:00 GMT+01:00 Daπid :
> On 16 December 2015 at 18:59, Francesc Alted wrote:
>
>> Probably MATLAB is shipping with Intel MKL enabled, which probably is the
>> fastest LAPACK implementation out there. NumPy supports linking with MKL,
>> and actually Anaconda does that by default, so s
On 16 December 2015 at 18:59, Francesc Alted wrote:
> Probably MATLAB is shipping with Intel MKL enabled, which probably is the
> fastest LAPACK implementation out there. NumPy supports linking with MKL,
> and actually Anaconda does that by default, so switching to Anaconda would
> be a good opt
What operating system are you on and how did you install numpy? From a
package manager, from source, by downloading from somewhere...?
On Dec 16, 2015 9:34 AM, "Edward Richards"
wrote:
> I recently did a conceptual experiment to estimate the computational time
> required to solve an exact express
On 16 Dec 2015, at 8:22 PM, Matthew Brett wrote:
>
>>> In [4]: %time testx = np.linalg.solve(testA, testb)
>>> CPU times: user 1min, sys: 468 ms, total: 1min 1s
>>> Wall time: 15.3 s
>>>
>>>
>>> so, it looks like you will need to buy a MKL license separately (which
>>> makes sense for a commerc
Hi,
On Wed, Dec 16, 2015 at 6:34 PM, Edison Gustavo Muenz
wrote:
> Sometime ago I saw this: https://software.intel.com/sites/campaigns/nest/
>
> I don't know if the "community" license applies in your case though. It is
> worth taking a look at.
>
> On Wed, Dec 16, 2015 at 4:30 PM, Francesc Alted
Continuum provides MKL free now - you just need to have a free anaconda.org
account to get the license: http://docs.continuum.io/mkl-optimizations/index
HTH,
Michael
On Wed, Dec 16, 2015 at 12:35 PM Edison Gustavo Muenz <
edisongust...@gmail.com> wrote:
> Sometime ago I saw this: https://softwar
Sometime ago I saw this: https://software.intel.com/sites/campaigns/nest/
I don't know if the "community" license applies in your case though. It is
worth taking a look at.
On Wed, Dec 16, 2015 at 4:30 PM, Francesc Alted wrote:
> Sorry, I have to correct myself, as per:
> http://docs.continuum.
Sorry, I have to correct myself, as per:
http://docs.continuum.io/mkl-optimizations/index it seems that Anaconda is
not linking with MKL by default (I thought that was the case before?).
After installing MKL (conda install mkl), I am getting:
In [1]: import numpy as np
Vendor: Continuum Analytics
Hi,
Probably MATLAB is shipping with Intel MKL enabled, which probably is the
fastest LAPACK implementation out there. NumPy supports linking with MKL,
and actually Anaconda does that by default, so switching to Anaconda would
be a good option for you.
Here you have what I am getting with Anacon
14 matches
Mail list logo