[Numpy-discussion] ANN: PyViennaCL 1.0.3 -- very easy GPGPU linear algebra
Toby St Clere Smithe
pyviennacl at tsmithe.net
Mon May 19 19:21:36 EDT 2014
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 !
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
> See the following link for documentation and example code:
> 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
> 2014-05-15 Toby St Clere Smithe <pyviennacl at tsmithe.net>
> * Release 1.0.3.
> * Update external/viennacl-dev to version 1.5.2.
> 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
> * Fix slicing of dense matrices (issue #18).
> * Enable test for matrix transposition
> * Add non-square matrix-vector product test
> 2014-05-06 Toby St Clere Smithe <pyviennacl at tsmithe.net>
> * Release 1.0.2.
> * Re-enable Lanczos algorithm for eigenvalues (issue #11).
> * Enable eigenvalue computations for compressed and coordinate
> * Fix matrix-vector product for non-square matrices (issue #13).
> * Link against rt on Linux (issue #12).
> Best regards,
Toby St Clere Smithe
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