[petsc-users] Configuring PETSc for KNL

Wed Apr 5 11:54:44 CDT 2017

> On Apr 5, 2017, at 10:53 AM, Jed Brown <jed at jedbrown.org> wrote:
> 
> "Zhang, Hong" <hongzhang at anl.gov> writes:
> 
>> On Apr 4, 2017, at 10:45 PM, Justin Chang <jychang48 at gmail.com<mailto:jychang48 at gmail.com>> wrote:
>> 
>> So I tried the following options:
>> 
>> -M 40
>> -N 40
>> -P 5
>> -da_refine 1/2/3/4
>> -log_view
>> -mg_coarse_pc_type gamg
>> -mg_levels_0_pc_type gamg
>> -mg_levels_1_sub_pc_type cholesky
>> -pc_type mg
>> -thi_mat_type baij
>> 
>> Performance improved dramatically. However, Haswell still beats out KNL but only by a little. Now it seems like MatSOR is taking some time (though I can't really judge whether it's significant or not). Attached are the log files.
>> 
>> 
>> MatSOR takes only 3% of the total time. Most of the time is spent on PCSetUp (~30%) and PCApply (~11%).
> 
> I don't see any of your conclusions in the actual data, unless you only
> looked at the smallest size that Justin tested.  For example, from the
> largest problem size in Justin's logs:

My mistake. I did not see the results for the large problem sizes. I was talking about the data for the smallest case.

Now I am very surprised by the performance of MatSOR:

-da_refine 1 ~2x slower on KNL
-da_refine 2 ~2x faster on KNL
-da_refine 3 ~2x faster on KNL
-da_refine 4 almost the same 

KNL

-da_refine 1 MatSOR              1185 1.0 2.8965e-01 1.1 7.01e+07 1.0 0.0e+00 0.0e+00 0.0e+00  3 41  0  0  0   3 41  0  0  0 15231
-da_refine 2 MatSOR              1556 1.0 1.6883e+00 1.0 5.82e+08 1.0 0.0e+00 0.0e+00 0.0e+00 11 44  0  0  0  11 44  0  0  0 22019
-da_refine 3 MatSOR              2240 1.0 1.4959e+01 1.0 5.51e+09 1.0 0.0e+00 0.0e+00 0.0e+00 22 45  0  0  0  22 45  0  0  0 23571
-da_refine 4 MatSOR              2688 1.0 2.3942e+02 1.1 4.47e+10 1.0 0.0e+00 0.0e+00 0.0e+00 36 45  0  0  0  36 45  0  0  0 11946

Haswell
-da_refine 1 MatSOR              1167 1.0 1.4839e-01 1.1 1.42e+08 1.0 0.0e+00 0.0e+00 0.0e+00  3 42  0  0  0   3 42  0  0  0 30450
-da_refine 2 MatSOR              1532 1.0 2.9772e+00 1.0 1.17e+09 1.0 0.0e+00 0.0e+00 0.0e+00 28 44  0  0  0  28 44  0  0  0 12539
-da_refine 3 MatSOR              1915 1.0 2.7142e+01 1.1 9.51e+09 1.0 0.0e+00 0.0e+00 0.0e+00 45 45  0  0  0  45 45  0  0  0 11216
-da_refine 4 MatSOR              2262 1.0 2.2116e+02 1.1 7.56e+10 1.0 0.0e+00 0.0e+00 0.0e+00 48 45  0  0  0  48 45  0  0  0 10936

Hong (Mr.)

> KNL:
> MatSOR              2688 1.0 2.3942e+02 1.1 4.47e+10 1.0 0.0e+00 0.0e+00 0.0e+00 36 45  0  0  0  36 45  0  0  0 11946
> KSPSolve               8 1.0 4.3837e+02 1.0 9.87e+10 1.0 1.5e+06 8.8e+03 5.0e+03 68 99 98 61 98  68 99 98 61 98 14409
> SNESSolve              1 1.0 6.1583e+02 1.0 9.95e+10 1.0 1.6e+06 1.4e+04 5.1e+03 96100100100 99  96100100100 99 10338
> SNESFunctionEval       9 1.0 3.8730e+01 1.0 0.00e+00 0.0 9.2e+03 3.2e+04 0.0e+00  6  0  1  1  0   6  0  1  1  0     0
> SNESJacobianEval      40 1.0 1.5628e+02 1.0 0.00e+00 0.0 4.4e+04 2.5e+05 1.4e+02 24  0  3 49  3  24  0  3 49  3     0
> PCSetUp               16 1.0 3.4525e+01 1.0 6.52e+07 1.0 2.8e+05 1.0e+04 3.8e+03  5  0 18 13 74   5  0 18 13 74   119
> PCSetUpOnBlocks       60 1.0 9.5716e-01 1.1 1.41e+05 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0     0
> PCApply               60 1.0 3.8705e+02 1.0 9.32e+10 1.0 1.2e+06 8.0e+03 1.1e+03 60 94 79 45 21  60 94 79 45 21 15407
> MatMult             2860 1.0 1.4578e+02 1.1 4.92e+10 1.0 1.2e+06 8.8e+03 0.0e+00 21 49 77 48  0  21 49 77 48  0 21579
> 
> Haswell:
> MatSOR              2262 1.0 2.2116e+02 1.1 7.56e+10 1.0 0.0e+00 0.0e+00 0.0e+00 48 45  0  0  0  48 45  0  0  0 10936
> KSPSolve               7 1.0 3.5937e+02 1.0 1.67e+11 1.0 6.7e+05 1.3e+04 4.5e+03 81 99 98 60 98  81 99 98 60 98 14828
> SNESSolve              1 1.0 4.3749e+02 1.0 1.68e+11 1.0 6.8e+05 2.1e+04 4.5e+03 99100100100 99  99100100100 99 12280
> SNESFunctionEval       8 1.0 1.5460e+01 1.0 0.00e+00 0.0 4.1e+03 4.7e+04 0.0e+00  3  0  1  1  0   3  0  1  1  0     0
> SNESJacobianEval      35 1.0 6.8994e+01 1.0 0.00e+00 0.0 1.9e+04 3.8e+05 1.3e+02 16  0  3 50  3  16  0  3 50  3     0
> PCSetUp               14 1.0 1.0860e+01 1.0 1.15e+08 1.0 1.3e+05 1.4e+04 3.4e+03  2  0 19 13 74   2  0 19 13 74   335
> PCSetUpOnBlocks       50 1.0 4.5601e-02 1.6 2.89e+05 0.0 0.0e+00 0.0e+00 0.0e+00  0  0  0  0  0   0  0  0  0  0     6
> PCApply               50 1.0 3.3545e+02 1.0 1.57e+11 1.0 5.3e+05 1.2e+04 9.7e+02 75 94 77 44 21  75 94 77 44 21 15017
> MatMult             2410 1.0 1.2050e+02 1.1 8.28e+10 1.0 5.1e+05 1.3e+04 0.0e+00 27 49 75 46  0  27 49 75 46  0 21983
> 
>> If ex48 has SSE2 intrinsics, does that mean Haswell would almost always be better?
>> 
>> The Jacobian evaluation (which has SSE2 intrinsics) on Haswell is about two times as fast as on KNL, but it eats only 3%-4% of the total time.
> 
> SNESJacobianEval alone accounts for 90 seconds of the 180 second
> difference between KNL and Haswell.
> 
>> According to your logs, the compute-intensive kernels such as MatMult,
>> MatSOR, PCApply run faster (~2X) on Haswell. 
> 
> They run almost the same speed.
> 
>> But since the setup time dominates in this test, 
> 
> It doesn't dominate on the larger sizes.
> 
>> Haswell would not show much benefit. If you increase the problem size,
>> it could be expected that the performance gap would also increase.
> 
> Backwards.  Haswell is great for low latency on small problem sizes
> while KNL offers higher theoretical throughput (often not realized due
> to lack of vectorization) for sufficiently large problem sizes
> (especially if they don't fit in Haswell L3 cache but do fit in MCDRAM).