- BLAS (Basic Linear Algebra Subprograms)
The Level 1 BLAS perform scalar, vector and vector-vector operations, the Level 2 BLAS perform matrix-vector operations, and the Level 3 BLAS perform matrix-matrix operations Because the BLAS are efficient, portable, and widely available, they are commonly used in the development of high quality linear algebra software, LAPACK for example
- What is the relation between BLAS, LAPACK and ATLAS
BLAS is a collection of low-level matrix and vector arithmetic operations (“multiply a vector by a scalar”, “multiply two matrices and add to a third matrix”, etc ) LAPACK is a collection of higher-level linear algebra operations Things like matrix factorizations (LU, LLt, QR, SVD, Schur, etc) that are used to do things like “find the eigenvalues of a matrix”, or “find the
- LAPACK — Linear Algebra PACKage
LAPACK routines are written so that as much as possible of the computation is performed by calls to the Basic Linear Algebra Subprograms (BLAS) LAPACK is designed at the outset to exploit the Level 3 BLAS — a set of specifications for Fortran subprograms that do various types of matrix multiplication and the solution of triangular systems with multiple right-hand sides Because of the
- Install openblas via apt-get `sudo apt-get install openblas-dev`
150 apt-cache search openblas libblas-test - Basic Linear Algebra Subroutines 3, testing programs libopenblas-base - Optimized BLAS (linear algebra) library based on GotoBLAS2 libopenblas-dev - Optimized BLAS (linear algebra) library based on GotoBLAS2 So sudo apt-get install libopenblas-dev solved the problem
- Sparse BLAS - Netlib
Sparse BLAS In the spirit of the dense BLAS, work is underway in the BLAS Technical Forum to establish a standard for the sparse BLAS The sparse BLAS interface addresses computational routines for unstructured sparse matrices Sparse BLAS also contains the three levels of operations as in the dense case
- blas - Can CMake FindBLAS find OpenBLAS? - Stack Overflow
message( STATUS BLAS found: ${BLAS_LIBRARIES} ) I have cblas, ATLAS and OpenBLAS including developer packages installed on a Debian Linux system, plus CMake 2 8 9
- FAQ - Netlib
The Level 1 BLAS perform scalar, vector and vector-vector operations, the Level 2 BLAS perform matrix-vector operations, and the Level 3 BLAS perform matrix-matrix operations Because the BLAS are efficient, portable, and widely available, they are commonly used in the development of high quality linear algebra software, LAPACK for example
- python - Limit number of threads in numpy - Stack Overflow
On Macs, export VECLIB_MAXIMUM_THREADS=1; see performance-of-numpy-with-different-blas-implementations on SO Beware: man Accelerate says "the value of VECLIB_MAXIMUM_THREADS may be cached by the library and reused; if you need to ensure single-threaded execution, you should set VECLIB_MAXIMUM_THREADS before making any Accelerate calls"
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