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MKL PARDISO - Large Scale Problem

6 Replies
Konstantin_A_Intel
222 Views
Hi Mital,
I would suggest you to try to use MKL version 10.3 first of all. A lot of significant improvements has been implemented since MKL 10.0 in PARDISO.
Regards,
Konstantin
Mistry__Mital
Beginner
222 Views
Thanks Konstantin,

I will do that and also, I tried following,

export MKL_PARDISO_OOC_MAX_CORE_SIZE=16000

And the process did not stop but it kept running for almost 2 days and did nothing.

Also, the process is not possible by in-core right?

Regards,

Mital
Mistry__Mital
Beginner
223 Views
Konstantin,

We have IA-64 architecture. So is there any special needs for updating to MKL 10.3 version?

Regards,
Mital
yanpu_z_
Beginner
223 Views

Hi, the workstation of mine is with 32G memery and two 6-core XEON E5-2630 cpu.

I wonder a sparse matrix with about 2million unknowns (roughly 20 million non-zero elements in the matrix) can be solved by the PARDISO  within reasonable time.

Thanks a lot! 

mecej4
Black Belt
223 Views

Yanpu:

I'm afraid that the answer is "It depends". Factorization of a highly sparse matrix (yours has a density of 0.000005) can be accompanied by substantial fill-in, and your system is far short of the memory that a dense matrix would take for the same number of unknowns.

You could try smaller problems and run tests to measure how the calculation scales, and project the results to the full size to obtain an estimate the run time.

Alexander_K_Intel2
223 Views

Hi yanpu.

You can try to use pardiso with iparm(60) is equal to 1. In such case if pardiso need to use more than 32Gb of RAM OOC algorithm will be used. Just set environment variable MKL_PARDISO_OOC_MAX_CORE_SIZE to 30000 to provide information about existence RAM to pardiso before PARDISO call.

With best regards,

Alexander Kalinkin

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