Intel® oneAPI Math Kernel Library
Ask questions and share information with other developers who use Intel® Math Kernel Library.
6538 Discussions

Inconsistency finding Eigenvalues for sparse matrices with MKL

Delgado_Schumann__An
235 Views

Hi,

I am trying to calculate the smallest real eigenvalues of a general problem A*V = B*V*D with sparse matrices using MKL and I have been having four problems when comparing to MATLAB:

1. I am always missing at least one of the smallest eigenvalues in MKL in comparison to MATLAB.

For example

Eigenvalues_Comparison_SecondCalculation.bmp

2. Using the same matrix most of the eigenvalues I do get are almost Identical between MATLAB and MKL, but which of the eigenvalues I am missing is somewhat random.

Same System, different results

Eigenvalues_Comparison_FirstCalculation.bmp

3. The smallest eigenvalue tend to be a several orders of magnitudes different between MATLAB and MKL (in some unfrequent cases not only the first)

MATLAB both Graphs: -0.000200            

MKL first graph:          380057.918617          

MKL second graph:     364053.320270

4. For some sparse matrices describing a very similar system with similar sparcity but different non-zero values I get no eigenvalues on MKL but several hundreds on MATLAB

I attached all data needed to reproduce my results in two Zips docs

Could somebody help me understand what I might be doing wrong or what alternative could I use to solve the problem

Thanks a lot

Andrés Delgado

0 Kudos
4 Replies
Gennady_F_Intel
Moderator
235 Views

Andres, is that 32 or 64-bit case? Did you link with the threaded version of mkl? is OpenMP or tbb? 

Gennady_F_Intel
Moderator
235 Views

please give us the reproducer shows how to you call mkl's routines 

Delgado_Schumann__An
235 Views

Hello Gennady,

We compile in x64. We do not want to run in parallel.

Following is the code used to call feast algorithm:


    _putenv("OMP_DYNAMIC=FALSE");
    _putenv("MKL_DYNAMIC=FALSE");
    mkl_set_num_threads(1);

    MKL_INT n = jDof;

    // CSR Matrix A
    double* a = toArray(env, jVals_A);
    MKL_INT* ia = toArray(env, jRows_A);
    MKL_INT* ja = toArray(env, jCols_A);
    
    // CSR Matrix B
    double* b = toArray(env, jVals_B);
    MKL_INT* ib = toArray(env, jRows_B);
    MKL_INT* jb = toArray(env, jCols_B);
    
    // search interval & subspace size for solution of eigenvalue problem
    double emin = jEmin;
    double emax = jEmax;
    MKL_INT m0 = jM0;

    // solution variables
    double* e = new double[m0];
    double* x = new double[m0*n];
    MKL_INT* m = new MKL_INT[1];
    double* res = new double[m0];
    
    // Eigen solver control parameters
    MKL_INT fpm[128];
    MKL_INT loop, info;
    double epsout;    

    /* -------------------------------------------------------------------- */
    /* .. Setup eigenvalue solver control parameters. */
    /* -------------------------------------------------------------------- */
    feastinit(fpm);

    fpm[0] = 1;
    fpm[26] = 0;              /* Check input matrices */
    fpm[27] = 0;              /* Check whether B matrix is positive definite */
    
    info = 0;                 /* Initialize info flag */
    const char* uplo = "F";   /* full matrices are provided*/
    
    dfeast_scsrgv(uplo, &n, a, ia, ja, b, ib, jb, fpm, &epsout, &loop, &emin, &emax, &m0, e, x, m, res, &info);

thank you for your interest in trying to help us

Best regards

Andrés

Delgado_Schumann__An
235 Views

Hello everybody,

do you have updates on my issue? I need a solution urgently.

Best regards

Andrés

 

Reply