Re: [Numpy-discussion] Mathematical functions in Numpy

[Shubhankar Mohapatra]

Hello all,I have submitted the proposal. It would be very nice if you would 
please give it a read and provide me with your feebacks. 

I think i can make a file with different functions from different libraries 
such as intels vml , amd acml  with the existing sleef and yeppp libraries.I 
understand that these functions may become outdated after sometime and some 
other faster function may come up. 
Then the only way out is to update the file after certain periods. Please 
advice me with other methods if there are any.And please also tell me, how an 
interface working between the libraries and Numpy will be better than an 
internal file containg the souce codes.
Thanks a lot for giving your time.



mshubhankar/gsoc2015

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 On Wednesday, 18 March 2015 1:31 AM, Julian Taylor 
jtaylor.deb...@googlemail.com wrote:
   

 currently the math functions are wrapped via the generic PyUfunc_*
functions in numpy/core/src/umath/loops.c.src which just apply some
arbitrary function to a scalar from arbitrarily strided inputs.
When adding variants one likely needs to add some special purpose loops
to deal with the various special requirements of the vector math api's.
This involves adding some special cases to the ufunc generation in
numpy/core/code_generators/generate_umath.py and then implementing the
new kernel functions.
See e.g. this oldish PR, which changes the sqrt function from a
PyUfunc_d_d function to a special loop to take advantage of the
vectorized machine instructions:
https://github.com/numpy/numpy/pull/3341

some things have changed a bit since then but it does show many of the
files you probably need to look for this project.

On 17.03.2015 19:51, Robert Kern wrote:
 On Tue, Mar 17, 2015 at 6:29 PM, Matthieu Brucher
 matthieu.bruc...@gmail.com mailto:matthieu.bruc...@gmail.com wrote:

 Hi,

 These functions are defined in the C standard library!
 
 I think he's asking how to define numpy ufuncs.
 
 2015-03-17 18:00 GMT+00:00 Shubhankar Mohapatra
 mshubhan...@yahoo.co.in mailto:mshubhan...@yahoo.co.in:
  Hello all,
  I am a undergraduate and i am trying to do a project this time on
 numppy in
  gsoc. This project is about integrating vector math library classes
 of sleef
  and yeppp into numpy to make the mathematical functions faster. I have
  already studied the new library classes but i am unable to find the
 sin ,
  cos function definitions in the numpy souce code.Can someone please
 help me
  find the functions in the source code so that i can implement the new
  library class into numpy.
  Thanking you,
  Shubhankar Mohapatra
 
 
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Re: [Numpy-discussion] GSoC students: please read

[Ralf Gommers]

On Mon, Mar 23, 2015 at 10:21 PM, Ralf Gommers ralf.gomm...@gmail.com
wrote:

 Hi all,

 It's great to see that this year there are a lot of students interested in
 doing a GSoC project with Numpy or Scipy. So far five proposals have been
 submitted, and it looks like several more are being prepared now. I'd like
 to give you a bit of advice as well as an idea of what's going to happen in
 the few weeks.

 The deadline for submitting applications is 27 March. Don't wait until the
 last day to submit your proposal! It has happened before that Melange was
 overloaded and unavailable - the Google program admins will not accept that
 as an excuse and allow you to submit later. So as soon as your proposal is
 in good shape, put it in. You can still continue revising it.

 From 28 March until 13 April we will continue to interact with you, as we
 request slots from the PSF and rank the proposals. We don't know how many
 slots we will get this year, but to give you an impression: for the last
 two years we got 2 slots. Hopefully we can get more this year, but that's
 far from certain.

 Our ranking will be based on a combination of factors: the interaction
 you've had with potential mentors and the community until now (and continue
 to have), the quality of your submitted PRs, quality and projected impact
 of your proposal, your enthusiasm, match with potential mentors, etc. We
 will also organize a video call (Skype / Google Hangout / ...) with each of
 you during the first half of April to be able to exchange ideas with a
 higher communication bandwidth medium than email.

 Finally a note on mentoring: we will be able to mentor all proposals
 submitted or suggested until now. Due to the large interest and technical
 nature of a few topics it has in some cases taken a bit long to provide
 feedback on draft proposals, however there are no showstoppers in this
 regard. Please continue improving your proposals and working with your
 potential mentors.


Hi all, just a heads up that I'll be offline until next Friday. Good luck
everyone with the last-minute proposal edits. I plan to contact all
students that submitted a GSoC application next weekend with more details
on what will happen next and see when we can schedule a call.

Cheers,
Ralf
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[Numpy-discussion] ANN: pyMIC v0.5 released

[Klemm, Michael]

Announcement: pyMIC v0.5
=

I'm happy to announce the release of pyMIC v0.5.

pyMIC is a Python module to offload computation in a Python program to the 
Intel Xeon Phi coprocessor.  It contains offloadable arrays and device 
management functions.  It supports invocation of native kernels (C/C++, 
Fortran) and blends in with Numpy's array types for float, complex, and int 
data types.

For more information and downloads please visit pyMIC's Github page: 
https://github.com/01org/pyMIC.  You can find pyMIC's mailinglist at 
https://lists.01.org/mailman/listinfo/pymic.


Full change log:
=

Version 0.5

- Introduced new kernel API that avoids insane pointer unpacking.
- pyMIC now uses libxstreams as the offload back-end
  (https://github.com/hfp/libxstream).
- Added smart pointers to make handling of fake pointers easier.

Version 0.4

- New low-level API to allocate, deallocate, and transfer data
  (see OffloadStream).
- Support for in-place binary operators.
- New internal design to handle offloads.

Version 0.3

- Improved handling of libraries and kernel invocation.
- Trace collection (PYMIC_TRACE=1, PYMIC_TRACE_STACKS={none,compact,full}).
- Replaced the device-centric API with a stream API.
- Refactoring to better match PEP8 recommendations.
- Added support for int(int64) and complex(complex128) data types.
- Reworked the benchmarks and examples to fit the new API.
- Bugfix: fixed syntax errors in OffloadArray.

Version 0.2

- Small improvements to the README files.
- New example: Singular Value Decomposition.
- Some documentation for the API functions.
- Added a basic testsuite for unit testing (WIP).
- Bugfix: benchmarks now use the latest interface.
- Bugfix: numpy.ndarray does not offer an attribute 'order'.
- Bugfix: number_of_devices was not visible after import.
- Bugfix: member offload_array.device is now initialized.
- Bugfix: use exception for errors w/ invoke_kernel  load_library.

Version 0.1

Initial release.

Intel GmbH
Dornacher Strasse 1
85622 Feldkirchen/Muenchen, Deutschland
Sitz der Gesellschaft: Feldkirchen bei Muenchen
Geschaeftsfuehrer: Christian Lamprechter, Hannes Schwaderer, Douglas Lusk
Registergericht: Muenchen HRB 47456
Ust.-IdNr./VAT Registration No.: DE129385895
Citibank Frankfurt a.M. (BLZ 502 109 00) 600119052

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[Numpy-discussion] Announcing Theano 0.7

[Pascal Lamblin]

===
 Announcing Theano 0.7
===

This is a release for a major version, with lots of new
features, bug fixes, and some interface changes (deprecated or
potentially misleading features were removed).

Upgrading to Theano 0.7 is recommended for everyone, but you should
first make sure that your code does not raise deprecation warnings with
the version you are currently using.

For those using the bleeding edge version in the git repository, we
encourage you to update to the `rel-0.7` tag.

What's New
--

Highlights:
 * Integration of CuDNN for 2D convolutions and pooling on supported GPUs
 * Too many optimizations and new features to count
 * Various fixes and improvements to scan
 * Better support for GPU on Windows
 * On Mac OS X, clang is used by default
 * Many crash fixes
 * Some bug fixes as well

Description
---
Theano is a Python library that allows you to define, optimize, and
efficiently evaluate mathematical expressions involving
multi-dimensional arrays. It is built on top of NumPy. Theano
features:

 * tight integration with NumPy: a similar interface to NumPy's.
   numpy.ndarrays are also used internally in Theano-compiled functions.
 * transparent use of a GPU: perform data-intensive computations up to
   140x faster than on a CPU (support for float32 only).
 * efficient symbolic differentiation: Theano can compute derivatives
   for functions of one or many inputs.
 * speed and stability optimizations: avoid nasty bugs when computing
   expressions such as log(1+ exp(x)) for large values of x.
 * dynamic C code generation: evaluate expressions faster.
 * extensive unit-testing and self-verification: includes tools for
   detecting and diagnosing bugs and/or potential problems.

Theano has been powering large-scale computationally intensive
scientific research since 2007, but it is also approachable
enough to be used in the classroom (IFT6266 at the University of Montreal).

Resources
-

About Theano:
http://deeplearning.net/software/theano/

Related projects:
http://github.com/Theano/Theano/wiki/Related-projects

About NumPy:
http://numpy.scipy.org/

About SciPy:
http://www.scipy.org/

Machine Learning Tutorial with Theano on Deep Architectures:
http://deeplearning.net/tutorial/

Acknowledgments
---

I would like to thank all contributors of Theano. For this particular
release, many people have helped, and to list them all would be
impractical.

I would also like to thank users who submitted bug reports.

Also, thank you to all NumPy and Scipy developers as Theano builds on
their strengths.

All questions/comments are always welcome on the Theano
mailing-lists ( http://deeplearning.net/software/theano/#community )

-- 
Pascal
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Re: [Numpy-discussion] ANN: pyMIC v0.5 released

[Jerome Kieffer]

Hi,

Interesting project. How close is the C++ kernel needed from OpenCL kernels ?
Is it directly portable ?

I have tested my OpenCL code (via pyopencl) on the Phi and I did not
get better performances than the dual-hexacore Xeon (i.e. ~2x slower than a 
GPU). 

Cheers
-- 
Jérôme Kieffer
Data analysis unit - ESRF
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