[SciPy-User] ANN: PyViennaCL 1.0.3 -- very easy GPGPU linear algebra

[Toby St Clere Smithe]

Just to say that, thanks to Matthew Brett, binary wheels for Mac OS X
are now available, for Python versions 2.7, 3.3, and 3.4. This means
that, if you're on that platform, you won't have to build from source!
As usual, just run `pip install pyviennacl`, and please report any
issues you encounter to https://github.com/viennacl/pyviennacl-dev/issues !

Thanks,

Toby


Toby St Clere Smithe <pyviennacl at tsmithe.net> writes:
> Hello everybody,
>
> I am pleased to announce the 1.0.3 release of PyViennaCL! This release
> fixes a number of important bugs, and improves performance on nVidia
> Kepler GPUs. The ChangeLog is below, and the associated ViennaCL version
> is 1.5.2.
>
>
> About PyViennaCL
> ================
>
> *PyViennaCL* aims to make fast, powerful GPGPU and heterogeneous
> scientific computing really transparently easy, especially for users
> already using NumPy for representing matrices.
>
> PyViennaCL does this by harnessing the `ViennaCL
> <http://viennacl.sourceforge.net/>`_ linear algebra and numerical computation
> library for GPGPU and heterogeneous systems, thereby making available to Python
> programmers ViennaCL’s fast *OpenCL* and *CUDA* algorithms. PyViennaCL does
> this in a way that is idiomatic and compatible with the Python community’s most
> popular scientific packages, *NumPy* and *SciPy*.
>
> PyViennaCL exposes the following functionality:
>
> * sparse (compressed, co-ordinate, ELL, and hybrid) and dense
>   (row-major and column-major) matrices, vectors and scalars on your
>   compute device using OpenCL;
> * standard arithmetic operations and mathematical functions;
> * fast matrix products for sparse and dense matrices, and inner and
>   outer products for vectors;
> * direct solvers for dense triangular systems;
> * iterative solvers for sparse and dense systems, using the BiCGStab,
>   CG, and GMRES algorithms;
> * iterative algorithms for eigenvalue estimation problems.
>
> PyViennaCL has also been designed for straightforward use in the context
> of NumPy and SciPy: PyViennaCL objects can be constructed using NumPy
> arrays, and arithmetic operations and comparisons in PyViennaCL are
> type-agnostic.
>
> See the following link for documentation and example code:
> http://viennacl.sourceforge.net/pyviennacl/doc/
>
>
> Get PyViennaCL
> ==============
>
> PyViennaCL is easily installed from PyPI.
>
> If you are on Windows, there are binaries for Python versions 2.7, 3.2,
> 3.3, and 3.4.
>
> If you are on Mac OS X and want to provide binaries, then please get in
> touch! Otherwise, the installation process will build PyViennaCL from
> source, which can take a while.
>
> If you are on Debian or Ubuntu, binaries are available in Debian testing
> and unstable, and Ubuntu utopic. Just run::
>
>     apt-get install python-pyviennacl python3-pyviennacl
>
> To install PyViennaCL from PyPI, make sure you've got a recent version
> of the *pip* package manager, and run::
>
>     pip install pyviennacl
>
>
> Bugs and support
> ================
>
> If you find a problem in PyViennaCL, then please report it at
> https://github.com/viennacl/pyviennacl-dev/issues
>
>
> ChangeLog
> =========
>
> 2014-05-15  Toby St Clere Smithe  <pyviennacl at tsmithe.net>
>
> 	* Release 1.0.3.
>
> 	* Update external/viennacl-dev to version 1.5.2.
> 	  [91b7589a8fccc92927306e0ae3e061d85ac1ae93]
>
> 	  This contains two important fixes: one for a build failure on
> 	  Windows (PyViennaCL issue #17) relating to the re-enabling of the
> 	  Lanczos algorithm in 1.0.2, and one for an issue relating to
> 	  missing support for matrix transposition in the ViennaCL scheduler
> 	  (PyViennaCL issue #19, ViennaCL issue #73).
>
> 	  This release is also benefitial for performance on nVidia Kepler
> 	  GPUs, increasing the performance of matrix-matrix multiplications
> 	  to 600 GFLOPs in single precision on a GeForce GTX 680.
>
> 	* Fix bug when using integers in matrix and vector index key
> 	  [dbb1911fd788e66475f5717c1692be49d083a506]
>
> 	* Fix slicing of dense matrices (issue #18).
> 	  [9c745710ebc2a1066c7074b6c5de61b227017cc6]
>
> 	* Enable test for matrix transposition
> 	  [9e951103b883a3848aa2115df3edce73d347c09b]
>
> 	* Add non-square matrix-vector product test
> 	  [21dd29cd10ebe02a96ee23c20ee55401bc6c874f]
>
> 2014-05-06  Toby St Clere Smithe  <pyviennacl at tsmithe.net>
>
> 	* Release 1.0.2.
>
> 	* Re-enable Lanczos algorithm for eigenvalues (issue #11).
> 	[cbfb41fca3fb1f3db42fd7b3ccb8332b701d1e20]
>
> 	* Enable eigenvalue computations for compressed and coordinate
>   	matrices.
> 	[8ecee3b200a92ae99b72653a823c1f60e62f75dd]
>
> 	* Fix matrix-vector product for non-square matrices (issue #13).
> 	[bf3aa2bf91339df72b6f7561afaf8b12aad57cda]
>
> 	* Link against rt on Linux (issue #12).
> 	[d5784b62b353ebbfd78fe1335fd96971b5089f53]
>
>
>
>
> Best regards,

-- 
Toby St Clere Smithe
http://tsmithe.net