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scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit, as well as interfaces to select functions in the CULA Dense Toolkit. Both low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy and Scipy are provided. Package documentation is available at http://scikit-cuda.readthedocs.org/. Many of the high-level functions have examples in their docstrings. More illustrations of how to use both the wrappers and high-level functions can be found in the demos/ and tests/ subdirectories.

gpu cuda blas lapack numericalTheano 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. Its features include tight integration with NumPy, transparent use of a GPU, dynamic C code generation and lot more.

deep-learning neural-network math numerical symbolic blas numpy gpu autodiff differentiationA high performance linear algebra library, written in JavaScript and optimized with C++ bindings to BLAS. The documentation is located in the wiki section of this repository.

blas matrix vector linear-algebra high-performance-computing machine-learning linear algebraBLIS is a portable software framework for instantiating high-performance BLAS-like dense linear algebra libraries. The framework was designed to isolate essential kernels of computation that, when optimized, immediately enable optimized implementations of most of its commonly used and computationally intensive operations. BLIS is written in ISO C99 and available under a new/modified/3-clause BSD license. While BLIS exports a new BLAS-like API, it also includes a BLAS compatibility layer which gives application developers access to BLIS implementations via traditional BLAS routine calls. An object-based API unique to BLIS is also available. For a thorough presentation of our framework, please read our journal article, "BLIS: A Framework for Rapidly Instantiating BLAS Functionality". For those who just want an executive summary, please see the next section.

blis blas linear-algebra linear-algebra-library matrix-multiplication matrix-calculations matrix-libraryArmadillo: fast C++ library for linear algebra & scientific computing - http://arma.sourceforge.net

linear-algebra matrix matrix-functions linear-algebra-library statistics matlab blas lapack hpc scientific-computing mkl machine-learning armadillo openmp gaussian-mixture-models cpp11 vector sparse-matrix expression-template matrix-factorizationA wrapper for NVidia's CuBLAS (Compute Unified Basic Linear Algebra Subprograms) for the CLR.

blas cuda gpgpu gpu hpc linear-algebra mathDodoni.net is a free/open-source library with the aim to provide a framework for quantitative finance and for numerical computing.

blas excel finance intel-mkl-wrapper lapack numerical-algorithmsAn implementation of linear algebra numerical structures and methods for the CLR. NPack is unique in that it uses generics for matrix element definitions, and a set of matrix operations via an interface, allowing a CLR-based operations engine as well as the opportunity to use ...

math matrix npack algorithms blas gpgpu gpuGeneral matrix multiply for ndarrays. This is analogous to the BLAS level 3 routine xGEMM.Note that while this implementation is correct, it is not yet very optimized. If someone wants to take over this project or suggest improvements, patches are welcome.

scijs matrix multiply ndarray blas level 3 general vector outer inner product linear algebra numerical methodsA quick note on why this exists: The goal is not to reinvent the wheel. There are lots of implementations of BLAS out there. Even for JS. There's a nodejs wrapper for LAPACK. Depending on what you need, maybe you should use that. The goal of this is to bring standardized BLAS operations to ndarrays so that algorithms can be made as future-resistant as possible by writing them in terms of standardized, easily-translatable operations.This library implements the basic vector operations of the Level 1 Basic Linear Algebra Subprograms (BLAS). Many of these functions are also implemented in ndarray-ops—which also has functions that are not included in BLAS. So the right answer is probably some blend of the two. This library exists mainly to frame things in a relatively standard, coherent framework.

blas ndarray scijs linear-algebraThis library implements the basic vector operations of the Level 1 Basic Linear Algebra Subprograms (BLAS). Many of these functions are also implemented in ndarray-ops—which also has functions that are not included in BLAS. So the right answer is probably some blend of the two. This library exists mainly to frame things in a relatively standard, coherent framework.NB: This library performs no checks to ensure you're only passing one-dimensional vectors. That's either a bug or a feature, depending on how you think about it.

blas complex ndarray scijs linearalgebraNormally Furious.js would automatically detect the optimal backend, but it is possible to specify it manually. If you plan to use Node-WebCL, you'll need to install the upstream version of Node-WebCL, and its dependencies.

ndarray array matrix tensor hpc computing blas science scientific numeric math mathematics statisticsThis is a Nim wrapper for the BLAS routines. You can import nimblas/cblas to use the standard BLAS interface, or just import nimblas for a version that is more Nim-friendly.

blas nim linear-algebraDBCSR is a library designed to efficiently perform sparse matrix matrix multiplication, among other operations. It is MPI and OpenMP parallel and can exploit GPUs via CUDA. Optionally, you can install libxsmm.

cp2k blas matrix-multiplication gemm cuda sparse-matrix openmp-parallelization mpitrying to collect all useful tutorials for famous C math and algebra libraries such as CBLAS, CLAPACK, GSL...

clapack cblas blas lapack lapacke mathematics algebraGLAS is a C library written in Dlang. No C++/D runtime is required but libc, which is available everywhere. CBLAS API can be provided by linking with Netlib's CBLAS library.

blas glas linear-algebra-subprograms algebra matrix-multiplication matrix lapack simdThis repository provides the Blis linear algebra routines as a self-contained Python C-extension. Clearly the Dell's numpy+OpenBLAS performance is the outlier, so it's likely something has gone wrong in the compilation and architecture detection.

cython blis blas blas-libraries openblas linear-algebra matrix-multiplication numpy neural-networks neural-networkxtensor-blas is an extension to the xtensor library, offering bindings to BLAS and LAPACK libraries through cxxblas and cxxlapack from the FLENS project. xtensor-blas currently provides non-broadcasting dot, norm (1- and 2-norm for vectors), inverse, solve, eig, cross, det, slogdet, matrix_rank, inv, cholesky, qr, svd in the xt::linalg namespace (check the corresponding xlinalg.hpp header for the function signatures). The functions, and signatures, are trying to be 1-to-1 equivalent to NumPy. Low-level functions to interface with BLAS or LAPACK with xtensor containers are also offered in the blas and lapack namespace.

blas linear-algebra xtensor lapack c-plus-plus-14This is a binding of BLAS/LAPACK for Numo::NArray using dynamic linking loader. This desgin allows you to change backend libraries without re-compiling. Install LAPACK or alternative package.

numo linalg narray lapack blas matrix linear-algebra-library
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