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From: Karl R. <ru...@iu...> - 2016-01-20 22:15:08
|
Dear ViennaCL users, ViennaCL 1.7.1 has just been released and is available for download at http://viennacl.sourceforge.net/ The highlights of this new bugfix release are: * Fixed performance regression with newer AMD drivers for dense matrix-matrix products on AMD GPUs. * trans() now takes arbitrary matrix expressions as input * Improved performance of y += Ax and y -= Ax for sparse matrix A * Better support for systems with maximum OpenCL workgroup size 1 * Runtime selection of best SpMV kernel on NVIDIA hardware * Improved OpenMP performance for BLAS level 1 and 2 operations A full list of changes is available here: http://viennacl.sourceforge.net/doc/changelog.html Many thanks to the community for the precious input! Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2015-07-31 15:57:27
|
Dear ViennaCL users, ViennaCL 1.7.0 has just been released. Due to a storage fault at Sourceforge [1], a download from the ViennaCL-Sourceforge page [2,3] is not yet possible. An alternative for the next few days is to download a release tarball from the developer repository on GitHub: https://github.com/viennacl/viennacl-dev/releases The highlights of the 1.7.0 release are: * Fine-grained parallel incomplete LU factorization preconditioners (based on recent paper by Chow and Patel [4]) * Fast sparse matrix-matrix multiplication (based on recent paper by Gremse et al. [5]) * Improved performance of sparse matrix-vector products * Fine-grained parallel algebraic multigrid preconditioners for CUDA, OpenCL, and OpenMP. * Conversion between vectors and matrices of different numeric type * Lanczos eigenvalue solver now optionally also returns eigenvectors. * Interface to/from the Armadillo library [4] A full list of changes is available here: http://viennacl.sourceforge.net/doc/changelog.html Updates to the benchmark section on the ViennaCL webpage in order to showcast the good performance will follow shortly. Best regards, Karl Rupp [1] https://sourceforge.net/blog/sourceforge-infrastructure-and-service-restoration-update-for-724/ [2] https://sourceforge.net/projects/viennacl/ [3] http://viennacl.sourceforge.net/ [4] http://epubs.siam.org/doi/abs/10.1137/140968896 [5] http://epubs.siam.org/doi/abs/10.1137/130948811 [6] http://arma.sourceforge.net/ |
From: Karl R. <ru...@iu...> - 2014-12-11 23:14:21
|
Dear ViennaCL users, ViennaCL 1.6.2 is now available for download at http://viennacl.sourceforge.net/ Most notably, this latest release provides full compatibility with the OpenMP 2.0 standard so that also specialized compilers on supercomputers can be used, resolves compilation problems with CUDA on Visual Studio, and further enhances the performance of pipelined iterative solvers. A full list of changes is available here: http://viennacl.sourceforge.net/doc/changelog.html Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2014-12-01 16:46:47
|
Dear ViennaCL users, it is my pleasure to announce the first release of ViennaCLBench, a benchmark GUI developed primarily by our Google Summer of Code student Namik Karovic. Chapeau! The GUI extensively benchmarks the following operations: - Dense matrix-matrix products (GEMM) - Sparse matrix-vector products (SpMV - with Matrix Market browser) - Vector operations (AXPY) - Host-Device bandwidth (PCI-Express, etc.) This provides a quick quantification of the performance achievable on a given machine. More operations are likely to be added in the future, subject to user feedback and contributions. Webpage: http://viennaclbench.sourceforge.net/ Download & Screenshots: https://sourceforge.net/projects/viennaclbench/ Developer Repository: https://github.com/viennacl/viennaclbench-dev License: MIT/X11 Precompiled binaries are available for Windows, Linux, and Mac OS. Builds from source are also reasonably convenient and documented, but more involved than just downloading the binaries. With best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2014-11-20 14:48:07
|
Dear ViennaCL users, ViennaCL 1.6.1 is now available for download at http://viennacl.sourceforge.net/ The highlight of this bugfix and performance enhancement release is the inclusion of our own implementation of a fast sparse matrix-vector product kernel for compressed_matrix<>, which will be presented at the Supercomputing Conference 2014 by colleagues from AMD shortly [1]. A full list of changes is available here: http://viennacl.sourceforge.net/doc/changelog.html Best regards, Karl Rupp [1] http://sc14.supercomputing.org/schedule/event_detail?evid=pap569 |
From: Karl R. <ru...@iu...> - 2014-11-08 23:28:06
|
Dear ViennaCL users, ViennaCL 1.6.0 is now available for download at http://viennacl.sourceforge.net/ This release is packed with many performance improvements and new features. The highlights are as follows: - Fully-integrated device database for portable high performance of OpenCL kernels. - Pipelined implementations of unpreconditioned iterative solvers which are up to three-fold faster [1] than other GPU-accelerated solver packages. - New sparse matrix type: sliced_ell_matrix. This new format was proposed for CPUs, GPUs, and MIC by Kreutzer et al. [2]. - Documentation now entirely based on HTML including embedded search. A full list of changes is available here: http://viennacl.sourceforge.net/doc/changelog.html Best regards, Karl Rupp [1] http://arxiv.org/abs/1410.4054 [2] http://arxiv.org/abs/1307.6209 |
From: Toby St C. S. <pyv...@ts...> - 2014-05-19 23:21:48
|
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 <pyv...@ts...> 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 <pyv...@ts...> > > * 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 <pyv...@ts...> > > * 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 |
From: Toby St C. S. <pyv...@ts...> - 2014-05-18 11:56:35
|
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 <pyv...@ts...> * 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 <pyv...@ts...> * 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 |
From: Karl R. <ru...@iu...> - 2014-05-15 21:47:10
|
Dear ViennaCL users, ViennaCL 1.5.2 is now available for download at http://viennacl.sourceforge.net/ While the work for the upcoming 1.6.0 release is in full progress, this maintenance release fixes a couple of bugs and performance regressions reported to us: - Fixed compilation problems on Visual Studio for the operations y += prod(A, x) and y -= prod(A, x) with dense matrix A. - Added a better performance profile for NVIDIA Kepler GPUs. For example, this increases the performance of matrix-matrix multiplications to 600 GFLOPs in single precision on a GeForce GTX 680. Thanks to Paul Dufort for bringing this to our attention. - Added support for the operation A = trans(B) for matrices A and B to the scheduler. - Fixed compilation problems in block-ILU preconditioners when passing block boundaries manually. - Ensured compatibility with OpenCL 1.0, which may still be available on older devices. Thanks a lot for the precious feedback! Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2014-03-05 11:14:35
|
Hello, ViennaCL is again participating in the Google Summer of Code (http://www.google-melange.com/gsoc/homepage/google/gsoc2014) via the Computational Science and Engineering at TU Wien organization. Project ideas and their descriptions can be found here: http://www.iue.tuwien.ac.at/cse/index.php/gsoc/2014.html For quick reference, here are the ideas in alphabetical order: - Benchmark GUI - GPU-Accelerated STL Functionality - Improving the PyViennaCL Python Wrapper - Sparse Matrix-Matrix Multiplication - Tuning the OpenMP backend We are also open to custom project proposals. If you're interested, please get in touch with us asap. Also, if you know of any other well-suited students, please spread the word. The incentives are (at least) the following: - Of course the salary by Google, of which USD 5500 go to the student. - A potential publication after successful completion of the project - Valuable experience in how open source projects work (soft-skill!) - Insight into multi- and many-core hardware (depending on the project) Important dates: - Application deadline for students: March 21, 2014 - Coding start: May 19, 2014 - Coding stop ('pencils down'): August 18, 2014 Best regards, Karli |
From: Toby St C. S. <pyv...@ts...> - 2014-02-20 11:54:57
|
Dear ViennaCL users, If you've ever used Python for your numerical applications, you know what joy it can be. Now, the easy power of ViennaCL 1.5.1 is at last married to that experience. I am pleased to announce the first release of PyViennaCL! Download links for source and Ubuntu binaries are found at the usual place: http://viennacl.sourceforge.net/viennacl-download.html * If you are or know anyone who could help with building PyViennaCL for other systems (Windows, Mac OS X, CentOS / RHEL, Fedora, SuSE, ...), please get in touch! See the following link for documentation and example code: http://viennacl.sourceforge.net/pyviennacl/doc/ PyViennaCL 1.0.0 exposes most of the functionality of ViennaCL: + 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. Some ViennaCL functionality is not yet available, and these features are planned for a release in the coming months: + preconditioners and QR factorization; + additional solvers and other algorithms, such as FFT computation; + structured matrices; + CUDA support (use OpenCL for now!); + advanced OpenCL integration. Spread the word! Toby St Clere Smithe |
From: Toby St C. S. <pyv...@ts...> - 2014-02-20 11:41:23
|
Dear ViennaCL users, If you've ever used Python for your numerical applications, you know what joy it can be. Now, the easy power of ViennaCL 1.5.1 is at last married to that experience. I am pleased to announce the first release of PyViennaCL! Download links for source and Ubuntu binaries are found at the usual place: http://viennacl.sourceforge.net/viennacl-download.html * If you are or know anyone who could help with building PyViennaCL for other systems (Windows, Mac OS X, CentOS / RHEL, Fedora, SuSE, ...), please get in touch! PyViennaCL 1.0.0 exposes most of the functionality of ViennaCL: + 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. Some ViennaCL functionality is not yet available, and these features are planned for a release in the coming months: + preconditioners and QR factorization; + additional solvers and other algorithms, such as FFT computation; + structured matrices; + CUDA support (use OpenCL for now!); + advanced OpenCL integration. Spread the word! Toby St Clere Smithe |
From: Karl R. <ru...@iu...> - 2014-01-20 22:15:07
|
Dear ViennaCL users, ViennaCL 1.5.1 is now available for download at http://viennacl.sourceforge.net/ It fixes a number of issues reported by our users: - Fixed a memory leak in the OpenCL kernel generator. Thanks to GitHub user dxyzab for spotting this. - Added compatibility of the mixed precision CG implementation with older AMD GPUs. Thanks to Andreas Rost for the input. - Fixed an error when running the QR factorization for matrices with less rows than columns. Thanks to Karol Polko for reporting. - Readded accidentally removed chapters on additional algorithms and structured matrices to the manual. Thanks to Sajjadul Islam for the hint. - Fixed buggy OpenCL kernels for matrix additions and subtractions for column-major matrices. Thanks to Tom Nicholson for reporting. - Fixed an invalid default kernel parameter set for matrix-matrix multiplications on CPUs when using the OpenCL backend. Thanks again to Tom Nicholson. - Corrected a weak check used in two tests. Thanks to Walter Mascarenhas for providing a fix. - Fixed a wrong global work size inside the SPAI preconditioner. Thanks to Andreas Rost. Thanks a lot for the precious feedback! Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2013-12-22 22:34:52
|
Dear ViennaCL users, ViennaCL 1.5.0 is finally out and available for download at http://viennacl.sourceforge.net/ It introduces a couple of new convenience routines, performance improvements, and higher code quality. Most important changes from the changelogs: - Vectors and matrices can be instantiated with integer template types (long, int, short, char). - Added support for element_prod() and element_div() for dense matrices. - Added element_pow() for vectors and matrices. - Added norm_frobenius() for computing the Frobenius norm of dense matrices. - Added unary element-wise operations for vectors and dense matrices: element_sin(), element_sqrt(), etc. - Multiple OpenCL contexts can now be used in a multi-threaded setting (one thread per context). - Multiple inner products with a common vector can now be computed efficiently via e.g.~inner_prod(x, tie(y, z)); - Added support for prod(A, B), where A is a sparse matrix type and B is a dense matrix (thanks to Albert Zaharovits for providing parts of the implementation). - Added diag() function for extracting the diagonal of a vector to a matrix, or for generating a square matrix from a vector with the vector elements on a diagonal (similar to MATLAB). - Added row() and column() functions for extracting a certain row or column of a matrix to a vector. - Certain BLAS functionality in ViennaCL is now also available through a shared library (libviennacl). - API-change: User-provided OpenCL kernels extract their kernels automatically. A call to add_kernel() is now obsolete, hence the function was removed. - API-change: Device class has been extend and supports all informations defined in the OpenCL 1.1 standard through member functions. Duplicate compute_units() and max_work_group_size() have been removed (thanks for Shantanu Agarwal for the input). - API-change: viennacl::copy() from a ViennaCL object to an object of non-ViennaCL type no longer tries to resize the object accordingly. An assertion is thrown if the sizes are incorrect in order to provide a consistent behavior across many different types. - Datastructure change: Vectors and matrices are now padded with zeros by default, resulting in higher performance particularly for matrix operations. This padding needs to be taken into account when using fast_copy(), particularly for matrices. The full change logs can be found at http://viennacl.sourceforge.net/changelog.txt Thanks to all contributors :-) Best regards and best wishes for 2014, Karl Rupp |
From: Karl R. <ru...@iu...> - 2013-04-29 03:06:20
|
Dear ViennaCL users, ViennaCL 1.4.2 is now available for download at http://viennacl.sourceforge.net/ This is a bugfix release, particularly addressing issues on Visual Studio 2012. From the changelogs: - Largely refactored the internal code base, unifying code for vector, vector_range, and vector_slice. Similar code refactoring was applied to matrix, matrix_range, and matrix_slice. This not only resolves the problems in VS 2012, but also leads to shorter compilation times and a smaller code base. - Improved performance of matrix-vector products of compressed_matrix on CPUs using OpenCL. - Resolved a bug which shows up if certain rows and columns of a compressed_matrix are empty and the matrix is copied back to host. - Fixed a bug and improved performance of GMRES. Thanks to Ivan Komarov for reporting via sourceforge. - Added additional Doxygen documentation. Thanks to all contributors :-) Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2013-03-16 18:47:51
|
Dear all, the ViennaCL developer repository moved to https://github.com/viennacl/viennacl-dev Please update your repository path in .git/config accordingly. Thanks and best regards, Karli |
From: Karl R. <ru...@iu...> - 2013-02-22 21:43:46
|
Dear ViennaCL users, ViennaCL 1.4.1 is now available for download! This release focuses on improved stability and performance on AMD devices rather than introducing new features. Highlights: - Included fast matrix-matrix multiplication kernel for AMD's Tahiti GPUs if matrix dimensions are a multiple of 128. Our sample HD7970 reaches over 1.3 TFLOPs in single precision and 200 GFLOPs in double precision. - Fixes and improved support for BLAS-1-type operations on dense matrices and vectors. - Vector expressions can now be passed to inner_prod(), norm_1(), norm_2() and norm_inf() directly. - Improved performance when using OpenMP. - Better support for Intel Xeon Phi (MIC). - The future development platform is now on github: https://github.com/karlrupp/viennacl-dev/ Thanks to all contributors :-) Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2012-12-03 03:26:25
|
Dear ViennaCL users, ViennaCL 1.4.0 is now available for download! The release features the largest number of additions, improvements, and cleanups since the very initial release. The most important changes are as follows: - Now three compute backends: CUDA, Host-based (single-threaded/OpenMP), and OpenCL - Added mixed-precision CG solver (OpenCL-based). - Greatly improved performance of ILU0/ILUT preconditioners (up to 10x). - Added initializer types from Boost.uBLAS (unit_vector, zero_vector, scalar_vector, identity_matrix, zero_matrix, scalar_matrix). - Added incomplete Cholesky factorization preconditioner. - Added element-wise operations for vectors as available in Boost.uBLAS (element_prod, element_div). - Added restart-after-N-cycles option to BiCGStab. - Added level-scheduling for ILU-preconditioners. Performance strongly depends on matrix pattern. - Reduced overhead when copying to/from ublas::compressed_matrix. - ViennaCL objects (scalar, vector, etc.) can now be used as global variables (thanks to an anonymous user on the support-mailinglist). - Kernel generator: Various new features, including support for multiple arguments, a repeat() feature for generating loops inside a kernel, element-wise products and division, and support for every one-argument OpenCL function. Thanks to all contributors :-) Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2012-08-09 21:06:58
|
Dear ViennaCL users, ViennaCL 1.3.1 is now available for download! Among smaller improvements, the noteworthy changes are: - Extended flexibility of submatrix and subvector proxies. - Block-ILU for compressed_matrix is now applied on the GPU during the solver cycle phase. - SVD now supports double precision. - Fixed a problem with matrix-matrix products if the result matrix is not initialized properly (thanks to Laszlo Marak). Thanks to all contributors :-) Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2012-07-27 10:36:16
|
Dear ViennaCL users, we have set up a new mailinglist for all developers and those who are willing to contribute and participate in the discussion of current and future directions. The current hot topic is the support for convenient multi-GPU usage, which brings new challenges from distributed memory environments (multi-GPU) to shared memory (CPU-RAM) systems. Please visit https://lists.sourceforge.net/lists/listinfo/viennacl-devel and subscribe. Best regards, Karli |
From: Karl R. <ru...@iu...> - 2012-07-03 08:58:01
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Dear ViennaCL users, due to frequent request we've set up a git repository on sourceforge.net last Friday. It will allow you to easily keep track of the latest developments in ViennaCL and to issue push requests for your own contributions. The release model will remain unchanged, i.e. we will continue to release well-tested packages in .tar.gz and .zip archives every couple of weeks/months. Best regards, Karli |
From: Karl R. <ru...@iu...> - 2012-05-14 08:07:46
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Dear ViennaCL users, ViennaCL 1.3.0 is now available for download! Highlights of the new release are the following features: - Addressing of sub-vectors and sub-vectors by means of vector_range and matrix_range now stable - QR factorization now stable - Two new sparse matrix formats (ell_matrix, hyb_matrix) for increased performance) - ILU0 and Block-ILU preconditioner - Automated OpenCL kernel generator for BLAS level 1 and BLAS level 2 operations. - Eigenvalue computations: Lanczos method, power iteration - Improved matrix-matrix multiplication performance, particularly on NVIDIA GPUs. Up to 300 GFLOPs on an NVIDIA GTX 580 (counting one fused multiply add as one FLOP) A full list of changes can be found in the manual at http://viennacl.sourceforge.net/ Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2012-03-22 22:06:47
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Dear ViennaCL users, ViennaCL 1.2.1 is now available for download! Highlights of the new release are the following features: - Fixed double precision problems on some (older) AMD GPUs. - Considerable improvements in the handling of matrix_range - Improved performance of matrix-matrix multiplication - Extended QR factorization, improved performance - Direct element access to compressed_matrix now possible - Fixed incorrect sizes when transferring data from non-square Eigen or MTL matrices Best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2012-01-01 12:33:18
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Dear ViennaCL users, ViennaCL 1.2.0 is now available for download! Highlights of the new release are the following features (all experimental): - Several algebraic multigrid preconditioners - Sparse approximate inverse preconditioners - Fast Fourier transform - Structured dense matrices (circulant, Hankel, Toeplitz, Vandermonde) - Reordering algorithms (Cuthill-McKee, Gibbs-Poole-Stockmeyer) - Proxies for manipulating subvectors and submatrices The features are expected to reach maturity in the 1.2.x branch. Happy New Year and best regards, Karl Rupp |
From: Karl R. <ru...@iu...> - 2011-05-21 20:22:42
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Dear ViennaCL users, version 1.1.2 is now available for download. The highlights of the new release are as follows: * Improved CG and BiCGStab performance especially for smaller systems * Improved performance of Jacobi and row scaling preconditioner setup * Improved performance of coordinate_matrix This is the final release of the 1.1.x family. The next version 1.2.0 will introduce a number of new features such as additional sparse matrix formats, support for integer vectors and matrices as well as better handling of multiple OpenCL platforms. Best regards, Karl Rupp |