what do I get if I build with MKL?
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)
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 <ndbecker2@gmail.com>
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)
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/
You also get highly optimized BLAS routines, like dgemm and degemv. From: numpy-discussion-bounces@scipy.org [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Matthieu Brucher Sent: Friday, April 19, 2013 9:39 AM To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] what do I get if I build with MKL? 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 <ndbecker2@gmail.com<mailto:ndbecker2@gmail.com>> 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) _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org<mailto:NumPy-Discussion@scipy.org> http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/ This email and any attachments are intended solely for the use of the individual or entity to whom it is addressed and may be confidential and/or privileged. If you are not one of the named recipients or have received this email in error, (i) you should not read, disclose, or copy it, (ii) please notify sender of your receipt by reply email and delete this email and all attachments, (iii) Dassault Systemes does not accept or assume any liability or responsibility for any use of or reliance on this email. For other languages, go to http://www.3ds.com/terms/email-disclaimer
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 <ndbecker2@gmail.com>
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?
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/
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 interface). Tom From: numpy-discussion-bounces@scipy.org [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Matthieu Brucher Sent: Friday, April 19, 2013 9:50 AM To: Discussion of Numerical Python Subject: Re: [Numpy-discussion] what do I get if I build with MKL? 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 <ndbecker2@gmail.com<mailto:ndbecker2@gmail.com>> 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?
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org<mailto:NumPy-Discussion@scipy.org> http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/ This email and any attachments are intended solely for the use of the individual or entity to whom it is addressed and may be confidential and/or privileged. If you are not one of the named recipients or have received this email in error, (i) you should not read, disclose, or copy it, (ii) please notify sender of your receipt by reply email and delete this email and all attachments, (iii) Dassault Systemes does not accept or assume any liability or responsibility for any use of or reliance on this email. For other languages, go to http://www.3ds.com/terms/email-disclaimer
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 KACVINSKY Tom <Tom.KACVINSKY@3ds.com>
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 interface).
Tom
*From:* numpy-discussion-bounces@scipy.org [mailto: numpy-discussion-bounces@scipy.org] *On Behalf Of *Matthieu Brucher *Sent:* Friday, April 19, 2013 9:50 AM
*To:* Discussion of Numerical Python *Subject:* Re: [Numpy-discussion] what do I get if I build with MKL?
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 <ndbecker2@gmail.com>
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?
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/
This email and any attachments are intended solely for the use of the individual or entity to whom it is addressed and may be confidential and/or privileged.
If you are not one of the named recipients or have received this email in error,
(i) you should not read, disclose, or copy it,
(ii) please notify sender of your receipt by reply email and delete this email and all attachments,
(iii) Dassault Systemes does not accept or assume any liability or responsibility for any use of or reliance on this email.
For other languages, go to http://www.3ds.com/terms/email-disclaimer
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/
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.brucher@gmail.com> wrote:
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 KACVINSKY Tom <Tom.KACVINSKY@3ds.com>
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 interface).
Tom
*From:* numpy-discussion-bounces@scipy.org [mailto: numpy-discussion-bounces@scipy.org] *On Behalf Of *Matthieu Brucher *Sent:* Friday, April 19, 2013 9:50 AM
*To:* Discussion of Numerical Python *Subject:* Re: [Numpy-discussion] what do I get if I build with MKL?
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 <ndbecker2@gmail.com>
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?
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/
This email and any attachments are intended solely for the use of the individual or entity to whom it is addressed and may be confidential and/or privileged.
If you are not one of the named recipients or have received this email in error,
(i) you should not read, disclose, or copy it,
(ii) please notify sender of your receipt by reply email and delete this email and all attachments,
(iii) Dassault Systemes does not accept or assume any liability or responsibility for any use of or reliance on this email.
For other languages, go to http://www.3ds.com/terms/email-disclaimer
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher Music band: http://liliejay.com/
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
participants (4)
-
KACVINSKY Tom -
Matthieu Brucher -
Neal Becker -
Xavier Barthelemy