blas

I am currently working on an application which involves lots and lots of calls to blas routines. Routinely checking for memory leaks I discovered, that I am loosing bytes in a dgemm call. The call looks like this: // I want to multiply 2 nxn matrices and put the result into C - an nxn matrix double zero = 0.0; double one = 1.0; double n; // matrix dimension char N = 'N'; dgemm_(&N, &N, &n, &n, &n, &one, A, &n, B, &n, &zero, C, &n); A,B and C are double fields of size n*n. The valgrind output is: ==78182== 18 bytes in 1 blocks are definitely lost in loss record 2 of 30 ==78182== at 0xB936:

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

>>> numpy.__config__.show() atlas_threads_info: NOT AVAILABLE blas_opt_info: libraries = ['f77blas', 'cblas', 'atlas'] library_dirs = ['/home/admin/anaconda/lib'] define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] language = c atlas_blas_threads_info: NOT AVAILABLE openblas_info: NOT AVAILABLE lapack_opt_info: libraries = ['lapack', 'f77blas', 'cblas', 'atlas'] library_dirs = ['/home/admin/anaconda/lib'] define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] language = f77 openblas_lapack_info: NOT AVAILABLE atlas_info: libraries = ['lapack', 'f77blas', 'cblas', 'atlas'] library_dirs = ['/home/admin

I’ve been looking into the Accelerate framework that was made available in iOS 4. Specifically, I made some attempts to use the Cblas routines in my linear algebra library in C. Now I can’t get the use of these functions to give me any performance gain over very basic routines. Specifically, the case of 4x4 matrix multiplication. Wherever I couldn’t make use of affine or homogeneous properties of the matrices, I’ve been using this routine (abridged): float *mat4SetMat4Mult(const float *m0, const float *m1, float *target) { target[0] = m0[0] * m1[0] + m0[4] * m1[1] + m0[8] * m1[2] + m0[12] * m1

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