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Paweł_J_
Beginner
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PARDISO Scalability

Hi,

I have a question about scalability of the PARDISO solver. I'm using Intel MKL PARDISO with my Finite Element code. I tested simple linear elastic problem with around 800 000 unknowns. The time results are:

 

threads pardiso phase 22 pardiso phase 33 sum pardiso speed up pardiso phase 22 speedup pardiso phase 33 speed up sum pardiso
1 1433.922 18.8455 1452.7675 1 1 1
2 720.3288 10.3364 730.6652 1.9906492702 1.8232169808 1.988280679
4 381.09201 7.709 388.80101 3.7626661341 2.4446101959 3.7365322173
6 285.75699 8.2753 294.03229 5.0179769881 2.2773192513 4.9408434019
8 234.9064 7.7265 242.6329 6.1042270453 2.4390733191 5.9875124107
10 225.3418 9.33 233.6718 6.3633200764 2.0198821008 6.2171280403
12 205.16 7.4491 212.6091 6.9892864106 2.5299029413 6.8330447756

 

I've tested it on a node with 2 processors with 6 cores Xeon X5650 with 2,66Ghz and 24 GB RAM DDR3 1333MHz .

I'm wondering if speed up around 7 is ok or I may get better speed up, when I play a little bit with input parameters for PARDISO?

Is there some articles or papers about scalability of the PARDISO solver? Could you pass me some links to them?

My input parameters are:

     iparm(1) = 1      
      iparm(3) = 0
      iparm(4) = 0
      iparm(5) = 0
      iparm(6) = 0

      iparm(7) = 0
      iparm(8) =10 
      iparm(9) = 0 
      iparm(10) = 8
      iparm(11) = 1
      iparm(12) = 0

      iparm(13) = 1
      iparm(14) = 0
      iparm(15) = 0
      iparm(16) = 0
      iparm(17) = 0
      iparm(18) = -1
      iparm(19) = 0 
      iparm(20) = 0
      iparm(21) = 1
      iparm(22) = 0
      iparm(23) = 0      
    iparm(24) = 0
      iparm(25) = 0 
      iparm(27) = 0 
      iparm(28) = 0
    iparm(30) = 0
    iparm(31) = 0
    iparm(35) = 0 
      iparm(60) =0

Thanks for all advices.

best regards,

Pawel J.

 

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2 Replies
Paweł_J_
Beginner
42 Views

Sorry my table was unreadable. I've attached screenshot from Calc.

best regards,
Pawel J.pardisoSpeedup.jpg

42 Views

Pawel,

  The speedup of around 7 seems to be fine. Since the matrix size is large, you may try the Cluster PARDISO from MKL.  Please find details here.

https://software.intel.com/en-us/articles/intel-math-kernel-library-parallel-direct-sparse-solver-fo...

--Vipin

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