Intel® Math Kernel Library

Installation fails --Jave Class Not Found -- on MacBook Pro

I am trying to install the Math Kernel Library under: 

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Academic Research Performance Libraries from Intel (OS X*)

 

On my MacBook Pro, when I try to start the install shell, I receive the following message:

./install.sh

In JPanelLicenseOptions

Inside JPanelRegistrationBegin

Calling initComponents

Calling initializePanel

Complex 1-D DFT not respecting DFTI_THREAD_LIMIT

Hi,

I recently noticed that when using a threaded 1-dimensional DFT, a DFTI_COMPLEX domain DFT does not appear to respect the DFTI_THREAD_LIMIT and instead always uses the threading value set by mkl_set_num_threads().  Furthermore, it appears that while a REAL domain DFT does obey DFTI_THREAD_LIMIT, its behavior has changed between MKL v11.1 and 11.2.

Slow rectangular matrix transposition ?

Hello,

I'm working with MKL 11.2.0.090 on Gentoo. I have an "Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz" processor.

I'm trying to speed my inplace matrix transpositions and for that I thought that mkl_?imatcopy would be the solution. I have a very speedup on square matrix, but on rectangular matrix it is much worse than my naive "follow the cycles" implementation.

Here is the call:

mkl_dimatcopy('R', 'T', rows, cols, 1.0, matrix_ptr, rows, cols);

When I profiled the executable, most of the cycles were spent in

PARDISO vs. RCI CG

Hi, 

I am trying to solve a relatively large system (100.000 equations) using either Pardiso or CG.

The system matrix is sparse, symmetric and converted to CSR format. For the matrix-vector multiplication

I use mkl_dcsrsymv for the RCI requests. My question is why Pardiso is way faster than CG? 

Shouldn't it be the other way around?

on a quad-core intel xeon it takes around 1 sec for pardiso, while CG needs around 30 sec.

These are the parameters i used on pardiso:

Significant Overhead if threaded MKL is called from OpenMP parallel region

Hello,

my aim is to diagonalize quadratic matrices with different sizes dxd in parallel. To this end I wrote a for  loop. In each iteration the aligned memory (dependent on the dimension d) is allocated with mkl_malloc(). The matrix is filled and afterwards dsyev is called to determine the optimal workspace size. Then I allocate the (aligned) workspace needed with mkl_malloc(), call dsyev once again to diagonalize the matrices and deallocate the memory that was used for the workspace and to store the matrix (using mkl_free()). 

Input error in Pardiso error_num= 15

Hi!

I am a new user. When I try to compile my fortran code in Pardiso. I got some errors.

 *** Error in Pardiso < sequence_ido,parameters> error_num= 15

*** Input check: matrix_type_new 0 <out of bounds>

*** Input parameters: inconsistent error= 15 max_fac_store_in:1

     matrix_number_in: 1 matrix_type_in: 0

     ido_in                   : 33 neqns_in       : 10000

     ia<neqns_in+1>-1: 0 nb_in              : 1

 

SOLVE COMPLETED ...

 

 

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