How to use sparse LDLT decomposition in MKL?

[NEROKAI]

Hi！

I want to use LDLT to decompose a large sparse matrix of mine. I read the manual of MKL and found that there is Bunch-Kaufman decomposition in LAPCAK, which can achieve the same effect as LDLT decomposition, but LAPCK in MKL seems to only support the decomposition of dense matrices .
Is there any way in MKL to decompose a large sparse matrix using LDLT?
Thank you, your help is invaluable to me!

[VidyalathaB_Intel · Accepted solution]

Hi Nero,

Thanks for reaching out to us.

Could you please let us know if using "Direct Sparse Solver (DSS) Interface Routines" matches your use case (dss_factor_real, dss_factor_complex - which also uses LDLT factorization)?

For more details please refer to the below link

https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top/sparse-solver-routines/direct-sparse-solver-dss-interface-routines.html

Regards,

Vidya.

[NEROKAI]

Thank you, I found this function in DSS, and I found that PARDISO can also be decomposed. May I ask you, is the efficiency of DSS and PARDISO the same?

[VidyalathaB_Intel]

Hi Nero,

>>I found this function in DSS, and I found that PARDISO can also be decomposed.is the efficiency of DSS and PARDISO the same?

Speaking in terms of efficiency, the MKL manual says Pardiso is efficient.

"The Intel® oneAPI Math Kernel Library PARDISO package is a high-performance, robust, memory efficient, and easy to use software package for solving large sparse linear systems of equations on shared-memory multiprocessors. "

Reference Link:

https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top/sparse-solver-routines/onemkl-pardiso-parallel-direct-sparse-solver-iface.html

I hope it answers your question.

Thanks for accepting the solution.

As the issue is resolved we are closing this thread. Please post a new question if you need any additional assistance from Intel as this thread will no longer be monitored.

Have a Nice Day!

Regards,

Vidya.