lapack

I wanted to install BLAS, CBLAS, LAPACK and OpenBLAS libraries from source using available packages you can download here openblas and lapack , blas/cblas . Firstly I removed my system blas/cblas and lapack libraries, but unfortunately atlas library couldn't be uninstalled (I can either have both blas and lapack or atlas - can't remove them all). I didn't bother and started compiling downloaded libraries cause I thought that after installation I would be able to remove atlas. Building process was based on this tutorial. For completeness I will list the steps: OpenBLAS . After editing Makefile

Some libraries such as BLAS/LAPACK or certain optimisation libraries get optimised for the local machine architecture upon compilation time. Lets take OpenBlas as an example. There exist two ways to create a Docker container with OpenBlas: Use a Dockerfile in which you specify a git clone of the OpenBlas library together with all necessary compilation flags and build commands. Pull and run someone else's image of Ubuntu + OpenBlas from the Docker Hub. Option (1) guarantees that OpenBlas is build and optimised for your machine. What about option (2)? As a Docker novice, I see images as

I am writing an algorithm in C that requires Matrix and Vector multiplications. I have a matrix Q (W x W) which is created by multiplying the transpose of a vector J (1 x W) with itself and adding Identity matrix I , scaled using scalar a . Q = [(J^T) * J + aI]. I then have to multiply the inverse of Q with vector G to get vector M . M = (Q^(-1)) * G. I am using cblas and clapack to develop my algorithm. When matrix Q is populated using random numbers (type float) and inverted using the routines sgetrf_ and sgetri_ , the calculated inverse is correct . But when matrix Q is symmetrical , which

Using Lapack with C++ is giving me a small headache. I found the functions defined for fortran a bit eccentric, so I tried to make a few functions on C++ to make it easier for me to read what's going on. Anyway, I'm not getting th matrix-vector product working as I wish. Here is a small sample of the program. smallmatlib.cpp: #include <cstdio> #include <stdlib.h> extern "C"{ // product C= alphaA.B + betaC void dgemm_(char* TRANSA, char* TRANSB, const int* M, const int* N, const int* K, double* alpha, double* A, const int* LDA, double* B, const int* LDB, double* beta, double* C, const int* LDC)

I'm trying to wrap the LAPACK function dgtsv (a solver for tridiagonal systems of equations) using Cython. I came across this previous answer , but since dgtsv is not one of the LAPACK functions that are wrapped in scipy.linalg I don't think I can use this particular approach. Instead I've been trying to follow this example . Here's the contents of my lapacke.pxd file: ctypedef int lapack_int cdef extern from "lapacke.h" nogil: int LAPACK_ROW_MAJOR int LAPACK_COL_MAJOR lapack_int LAPACKE_dgtsv(int matrix_order, lapack_int n, lapack_int nrhs, double * dl, double * d, double * du, double * b,

I'm building numpy from source on CentOS 6.5 with no root access (python -V=2.7.6). I have the latest numpy source from git. I cannot for the life of me get numpy to acknowledge atlas libs. I have: ls -1 /usr/lib64/atlas libatlas.so.3 libatlas.so.3.0 libcblas.so.3 libcblas.so.3.0 libclapack.so.3 libclapack.so.3.0 libf77blas.so.3 libf77blas.so.3.0 liblapack.so.3 liblapack.so.3.0 libptcblas.so.3 libptcblas.so.3.0 libptf77blas.so.3 libptf77blas.so.3.0 I don't know anything about how these libs came about, but I can only assume that the atlas builds would be faster than any standard BLAS/LAPACK