About ViennaCL
ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and...
ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects.
Features
- Three computing backends: CUDA, OpenCL, OpenMP
- Iterative Solvers: Conjugate Gradient, Stabilized BiConjugate Gradient, Generalized Minimum Residual
- Preconditioners: ICHOL, ILUT, ILU0, Block-ILU, AMG, (F)SPAI, Jacobi
- BLAS Level 1, Level 2 and Level 3 routines on GPUs and multi-core CPUs
- Fast sparse matrix-vector and sparse matrix-matrix products
- Convenient C++ wrappers for common linear algebra operations
- Fast Fourier transform
- C++ Interface is mostly uBLAS compatible
- Interfaces for uBLAS, Armadillo, Eigen and MTL 4
- Iterative Solvers can directly be used with uBLAS, Armadillo, Eigen and MTL4 objects
- Structured matrices: Circulant, Hankel, Toeplitz, Vandermonde
- OpenCL Kernel optimization environment for optimal performance on the target device
- Header-only library
- MATLAB interface for the iterative solvers (separate download)
- Python interface (PyViennaCL)
Previous Versions
Here you can find the changelog of ViennaCL since it was posted on our website on 2015-04-28 03:00:00.
The latest version is 1.7 and it was updated on 2024-04-22 16:00:30. See below the changes in each version.
ViennaCL version 1.7
Updated At: 2016-01-20
ViennaCL version files
Updated At: 2011-03-17
Changes: Several fixes and updates
Disclaimer
External Download
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