# Intel® Math Kernel Library 11.1 Update 2 is now available

Intel® Math Kernel Library (Intel® MKL) is a highly optimized, extensively threaded, and thread-safe library of mathematical functions for engineering, scientific, and financial applications that require maximum performance. The Intel MKL 11.1 Update 2 packages are now ready for download.

# Intel® MKL with Numpy, Scipy, Matlab, C#, Python, NAG and more

The following article explains on using Intel® MKL with Numpy/SciPy, Matlab, C#, Java, Python, NAG, Gromacs, Gnu Octave, PETSc, HPL, HPCC, IMSL etc.
• Apple OS X*
• Linux*
• Microsoft Windows* (XP, Vista, 7)
• .NET*
• C#
• C/C++
• Fortran
• Java*
• Python*
• Avancé
• Débutant
• Intermédiaire
• Bibliothèque Intel® Math Kernel Library
• scipy
• numpy
• matlab mkl
• python mkl
• C# mkl
• java mkl
• hpl mkl
• nag mkl
• hpcc mkl
• imsl mkl
• gromacs mkl
• wrf mkl
• Outils de développement
• Code source libre
• Optimisation
• # vsrnguniform

Dear all,

Can vsrnguniform generate random multidimensional arrays or it just works for 1D arrays? I guess that one solution could be to generate a 1D array and after reshape it. However, I would like to know if this function can handle multidimensional arrays?

Crist.

# problem in computation of 2D FFT using two seperate real and imaginary arrays

Hi,

i want to compute FFT of a complex 2D array. So first i have tried it in 2 ways.

1) Directly using complex 2D array(real and imaginary interleaved) .

2) 2 seperate arrays(where real and imaginary are deinterleaved into 2 seperate arrays).

i found the output is different in both cases. can some one tell me if i do some thing wrong in the code.

# Problem with robust estimation of a covariance matrix.

Hi I have the matrix "x" and I want to compute the covariance matrix. The i column of the matrix stores the observations
of the i variable.

The matrix is
0.8147    0.9058
0.1270    0.9134
0.6324    0.0975
and the true covariance matrix is
0.1269   -0.0450
-0.0450    0.2198

I read the manual Summary Statistics Application Notes (page 32) that explains how to find  a Robust Estimation of a Variance- -
Covariance Matrix and I wrote the following code in C.

# Scalapack on Mac OS X

What are the chances of seeing scalapack in mkl for Mac OS X in near future?

# blacs for sgi mpt

I need to compile a software package which requires blacs, however since about composer_xe_2013.5.192 blacs seems to have disappeared from MKL (except blacs for intelmpi and MIC). Where do I get the libmkl_blacs_sgimpt_lp64.a for intel64 for the newest composer_xe?

# mkl_dcsrmv slower than openMP implementation

Hi,

I'm trying to find the fastest way to do a multithreaded sparse matrix-vector multiply. I've written some benchmarking code to form a large random sparse matrix in CSR format, and then time 3 different implementations to compute y = y + A*x. I have a serial implementation, an openMP implementation, and mkl_dcsrmv. I'm computing the average and minimum time over a number of runs, say, 10.

# Eigenvalue decomposition with CSR sparse matrix

Hi,

at the moment I use dsyevd to compute the eigenvalues and eigenvectors of a large matrix A (n = 22000). This takes about half an hour. I know that they are a lot of zeros in matrix A (90% are zeros). Matrix A is stored as CSR sparse matrix.

- Is there a function to compute the eigenvalues and eigenvectors of a CSR sparse matrix?

- Is there a function to convert a CSR sparse matrix to a band matrix? Then I could use dsbevd.

Regards Michael