Jed Brown <[email protected]> writes:
> Fande Kong <[email protected]> writes:
>
>>> There's a lot more to AMG setup than memory bandwidth (architecture
>>> matters a lot, even between different generation CPUs).
>>
>>
>> Could you elaborate a bit more on this? From my understanding, one big part
>> of AMG SetUp is RAP that should be pretty much bandwidth.
>
> The RAP isn't "pretty much bandwidth". See below for some
> Skylake/POWER9/EPYC results and analysis (copied from an off-list
> thread). I'll leave in some other bandwidth comments that may or may
> not be relevant to you. The short story is that Skylake and EPYC are
> both much better than POWER9 at MatPtAP despite POWER9 having similar
> bandwidth as EPYC and thus being significantly faster than Skylake for
> MatMult/smoothing.
>
>
> Jed Brown <[email protected]> writes:
>
>> I'm attaching a log from my machine (Noether), which is 2-socket EPYC
>> 7452 (32 cores each). Each socket has 8xDDR4-3200 and 128 MB of L3
>> cache. This is the same node architecture as the new BER/E3SM machine
>> being installed at Argonne (though that one will probably have
>> higher-clocked and/or more cores per socket). Note that these CPUs are
>> about $2k each while Skylake 8180 are about $10k.
>>
>> Some excerpts/comments below.
>>
>
> [...]
>
> In addition to the notes below, I'd like to call out how important
> streaming stores are on EPYC. With vanilla code or _mm256_store_pd, we
> get the following performance
>
> $ mpiexec -n 64 --bind-to core --map-by core:1
> src/benchmarks/streams/MPIVersion
> Copy 162609.2392 Scale 159119.8259 Add 174687.6250 Triad 175840.1587
>
> but replacing _mm256_store_pd with _mm256_stream_pd gives this
>
> $ mpiexec -n 64 --bind-to core --map-by core:1
> src/benchmarks/streams/MPIVersion
> Copy 259951.9936 Scale 259381.0589 Add 250216.3389 Triad 249292.9701
I turned on NPS4 (a BIOS setting that creates a NUMA node for each pair
of memory channels) and get a modest performance boost.
$ mpiexec -n 64 --bind-to core --map-by core:1
src/benchmarks/streams/MPIVersion
Copy 289645.3776 Scale 289186.2783 Add 273220.0133 Triad 272911.2263
On this architecture, best performance comes from one process per 4-core CCX
(shared L3).
$ mpiexec -n 16 --bind-to core --map-by core:4
src/benchmarks/streams/MPIVersion
Copy 300704.8859 Scale 304556.3380 Add 295970.1132 Triad 298891.3821
> This is just preposterously huge, but very repeatable using gcc and
> clang, and inspecting the assembly. This suggests that it would be
> useful for vector kernels to have streaming and non-streaming variants.
> That is, if I drop the vector length by 20 (so the working set is 2.3
> MB/core instead of 46 MB in the default version), then we get 2.4 TB/s
> Triad with _mm256_store_pd:
>
> $ mpiexec -n 64 --bind-to core --map-by core:1
> src/benchmarks/streams/MPIVersion
> Copy 2159915.7058 Scale 2212671.7087 Add 2414758.2757 Triad
> 2402671.1178
>
> and a thoroughly embarrassing 353 GB/s with _mm256_stream_pd:
>
> $ mpiexec -n 64 --bind-to core --map-by core:1
> src/benchmarks/streams/MPIVersion
> Copy 235934.6653 Scale 237446.8507 Add 352805.7288 Triad 352992.9692
>
>
> I don't know a good way to automatically determine whether to expect the
> memory to be in cache, but we could make it a global (or per-object)
> run-time selection.
>
>> Jed Brown <[email protected]> writes:
>>
>>> "Smith, Barry F." <[email protected]> writes:
>>>
>>>> Thanks. The PowerPC is pretty crappy compared to Skylake.
>>>
>>> Compare the MGSmooth times. The POWER9 is faster than the Skylake
>>> because it has more memory bandwidth.
>>>
>>> $ rg 'MGInterp Level 4|MGSmooth Level 4' ex56*
>>> ex56-JLSE-skylake-56ranks-converged.txt
>>> 254:MGSmooth Level 4 68 1.0 1.8808e+00 1.2 7.93e+08 1.3 3.6e+04
>>> 1.9e+04 3.4e+01 8 29 10 16 3 62 60 18 54 25 22391
>>> 256:MGInterp Level 4 68 1.0 4.0043e-01 1.8 1.45e+08 1.3 2.2e+04
>>> 2.5e+03 0.0e+00 1 5 6 1 0 9 11 11 4 0 19109
>>>
>>> ex56-summit-cpu-36ranks-converged.txt
>>> 265:MGSmooth Level 4 68 1.0 1.1531e+00 1.1 1.22e+09 1.2 2.3e+04
>>> 2.6e+04 3.4e+01 3 29 7 13 3 61 60 12 54 25 36519 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 267:MGInterp Level 4 68 1.0 2.0749e-01 1.1 2.23e+08 1.2 1.4e+04
>>> 3.4e+03 0.0e+00 0 5 4 1 0 11 11 7 4 0 36925 0 0
>>> 0.00e+00 0 0.00e+00 0
>>>
>>> ex56-summit-gpu-24ranks-converged.txt
>>> 275:MGSmooth Level 4 68 1.0 1.4499e-01 1.2 1.85e+09 1.2 1.0e+04
>>> 5.3e+04 3.4e+01 0 29 7 13 3 26 60 12 55 25 299156 940881 115
>>> 2.46e+01 116 8.64e+01 100
>>> 277:MGInterp Level 4 68 1.0 1.7674e-01 1.0 3.23e+08 1.2 6.1e+03
>>> 6.7e+03 0.0e+00 0 5 4 1 0 33 11 7 4 0 42715 621223 36
>>> 2.98e+01 136 3.95e+00 100
>>>
>>> ex56-summit-gpu-36ranks-converged.txt
>>> 275:MGSmooth Level 4 68 1.0 1.4877e-01 1.2 1.25e+09 1.2 2.3e+04
>>> 2.6e+04 3.4e+01 0 29 7 13 3 19 60 12 54 25 291548 719522 115
>>> 1.83e+01 116 5.80e+01 100
>>> 277:MGInterp Level 4 68 1.0 2.4317e-01 1.0 2.20e+08 1.2 1.4e+04
>>> 3.4e+03 0.0e+00 0 5 4 1 0 33 11 7 4 0 31062 586044 36
>>> 1.99e+01 136 2.82e+00 100
>>
>> 258:MGSmooth Level 4 68 1.0 9.6950e-01 1.3 6.15e+08 1.3 4.0e+04 1.4e+04
>> 2.0e+00 6 28 10 15 0 59 59 18 54 25 39423
>> 260:MGInterp Level 4 68 1.0 2.5707e-01 1.5 1.23e+08 1.2 2.7e+04 1.9e+03
>> 0.0e+00 1 5 7 1 0 13 12 12 5 0 29294
>>
>> Epyc is faster than Power9 is faster than Sklake.
>>
>>>
>>> The Skylake is a lot faster at PtAP. It'd be interesting to better
>>> understand why. Perhaps it has to do with caching or aggressiveness of
>>> out-of-order execution.
>>>
>>> $ rg 'PtAP' ex56*
>>> ex56-JLSE-skylake-56ranks-converged.txt
>>> 164:MatPtAP 4 1.0 1.4214e+00 1.0 3.94e+08 1.5 1.1e+04
>>> 7.4e+04 4.4e+01 6 13 3 20 4 8 28 8 39 5 13754
>>> 165:MatPtAPSymbolic 4 1.0 8.3981e-01 1.0 0.00e+00 0.0 6.5e+03
>>> 7.3e+04 2.8e+01 4 0 2 12 2 5 0 5 23 3 0
>>> 166:MatPtAPNumeric 4 1.0 5.8402e-01 1.0 3.94e+08 1.5 4.5e+03
>>> 7.5e+04 1.6e+01 2 13 1 8 1 3 28 3 16 2 33474
>>>
>>> ex56-summit-cpu-36ranks-converged.txt
>>> 164:MatPtAP 4 1.0 3.9077e+00 1.0 5.89e+08 1.4 1.6e+04
>>> 7.4e+04 4.4e+01 9 13 5 26 4 11 28 12 46 5 4991 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 165:MatPtAPSymbolic 4 1.0 1.9525e+00 1.0 0.00e+00 0.0 1.2e+04
>>> 7.3e+04 2.8e+01 5 0 4 19 3 5 0 9 34 3 0 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 166:MatPtAPNumeric 4 1.0 1.9621e+00 1.0 5.89e+08 1.4 4.0e+03
>>> 7.5e+04 1.6e+01 5 13 1 7 1 5 28 3 12 2 9940 0 0
>>> 0.00e+00 0 0.00e+00 0
>>>
>>> ex56-summit-gpu-24ranks-converged.txt
>>> 167:MatPtAP 4 1.0 5.7210e+00 1.0 8.48e+08 1.3 7.5e+03
>>> 1.3e+05 4.4e+01 8 13 5 25 4 11 28 12 46 5 3415 0 16
>>> 3.36e+01 4 6.30e-02 0
>>> 168:MatPtAPSymbolic 4 1.0 2.8717e+00 1.0 0.00e+00 0.0 5.5e+03
>>> 1.3e+05 2.8e+01 4 0 4 19 3 5 0 9 34 3 0 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 169:MatPtAPNumeric 4 1.0 2.8537e+00 1.0 8.48e+08 1.3 2.0e+03
>>> 1.3e+05 1.6e+01 4 13 1 7 1 5 28 3 12 2 6846 0 16
>>> 3.36e+01 4 6.30e-02 0
>>>
>>> ex56-summit-gpu-36ranks-converged.txt
>>> 167:MatPtAP 4 1.0 4.0340e+00 1.0 5.89e+08 1.4 1.6e+04
>>> 7.4e+04 4.4e+01 8 13 5 26 4 11 28 12 46 5 4835 0 16
>>> 2.30e+01 4 5.18e-02 0
>>> 168:MatPtAPSymbolic 4 1.0 2.0355e+00 1.0 0.00e+00 0.0 1.2e+04
>>> 7.3e+04 2.8e+01 4 0 4 19 3 5 0 9 34 3 0 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 169:MatPtAPNumeric 4 1.0 2.0050e+00 1.0 5.89e+08 1.4 4.0e+03
>>> 7.5e+04 1.6e+01 4 13 1 7 1 5 28 3 12 2 9728 0 16
>>> 2.30e+01 4 5.18e-02 0
>>
>> 153:MatPtAPSymbolic 4 1.0 7.6053e-01 1.0 0.00e+00 0.0 7.6e+03 5.8e+04
>> 2.8e+01 5 0 2 12 2 6 0 5 22 3 0
>> 154:MatPtAPNumeric 4 1.0 6.5172e-01 1.0 3.21e+08 1.4 6.4e+03 4.8e+04
>> 2.4e+01 4 14 2 8 2 5 27 4 16 2 28861
>>
>> Epyc similar to Skylake here.
>>
>>> I'd really like to compare an EPYC for these operations. I bet it's
>>> pretty good. (More bandwidth than Skylake, bigger caches, but no
>>> AVX512.)
>>>
>>>> So the biggest consumer is MatPtAP I guess that should be done first.
>>>>
>>>> It would be good to have these results exclude the Jacobian and
>>>> Function evaluation which really dominate the time and add clutter making
>>>> it difficult to see the problems with the rest of SNESSolve.
>>>>
>>>>
>>>> Did you notice:
>>>>
>>>> MGInterp Level 4 68 1.0 1.7674e-01 1.0 3.23e+08 1.2 6.1e+03 6.7e+03
>>>> 0.0e+00 0 5 4 1 0 33 11 7 4 0 42715 621223 36 2.98e+01 136
>>>> 3.95e+00 100
>>>>
>>>> it is terrible! Well over half of the KSPSolve time is in this one
>>>> relatively minor routine. All of the interps are terribly slow. Is it
>>>> related to the transpose multiple or something?
>>>
>>> Yes, it's definitely the MatMultTranspose, which must be about 3x more
>>> expensive than restriction even on the CPU. PCMG/PCGAMG should
>>> explicitly transpose (unless the user sets an option to aggressively
>>> minimize memory usage).
>>>
>>> $ rg 'MGInterp|MultTrans' ex56*
>>> ex56-JLSE-skylake-56ranks-converged.txt
>>> 222:MatMultTranspose 136 1.0 3.5105e-01 3.7 7.91e+07 1.3 2.5e+04
>>> 1.3e+03 0.0e+00 1 3 7 1 0 5 6 13 3 0 11755
>>> 247:MGInterp Level 1 68 1.0 3.3894e-04 2.2 2.35e+05 0.0 0.0e+00
>>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 693
>>> 250:MGInterp Level 2 68 1.0 1.1212e-0278.0 1.17e+06 0.0 1.8e+03
>>> 7.7e+02 0.0e+00 0 0 1 0 0 0 0 1 0 0 2172
>>> 253:MGInterp Level 3 68 1.0 6.7105e-02 5.3 1.23e+07 1.8 2.7e+04
>>> 4.2e+02 0.0e+00 0 0 8 0 0 1 1 14 1 0 8594
>>> 256:MGInterp Level 4 68 1.0 4.0043e-01 1.8 1.45e+08 1.3 2.2e+04
>>> 2.5e+03 0.0e+00 1 5 6 1 0 9 11 11 4 0 19109
>>>
>>> ex56-summit-cpu-36ranks-converged.txt
>>> 229:MatMultTranspose 136 1.0 1.4832e-01 1.4 1.21e+08 1.2 1.9e+04
>>> 1.5e+03 0.0e+00 0 3 6 1 0 6 6 10 3 0 27842 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 258:MGInterp Level 1 68 1.0 2.9145e-04 1.5 1.08e+05 0.0 0.0e+00
>>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 370 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 261:MGInterp Level 2 68 1.0 5.7095e-03 1.5 9.16e+05 2.5 2.4e+03
>>> 7.1e+02 0.0e+00 0 0 1 0 0 0 0 1 0 0 4093 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 264:MGInterp Level 3 68 1.0 3.5654e-02 2.8 1.77e+07 1.5 2.3e+04
>>> 3.9e+02 0.0e+00 0 0 7 0 0 1 1 12 1 0 16095 0 0
>>> 0.00e+00 0 0.00e+00 0
>>> 267:MGInterp Level 4 68 1.0 2.0749e-01 1.1 2.23e+08 1.2 1.4e+04
>>> 3.4e+03 0.0e+00 0 5 4 1 0 11 11 7 4 0 36925 0 0
>>> 0.00e+00 0 0.00e+00 0
>>>
>>> ex56-summit-gpu-24ranks-converged.txt
>>> 236:MatMultTranspose 136 1.0 2.1445e-01 1.0 1.72e+08 1.2 9.5e+03
>>> 2.6e+03 0.0e+00 0 3 6 1 0 39 6 11 3 0 18719 451131 8
>>> 3.11e+01 272 2.19e+00 100
>>> 268:MGInterp Level 1 68 1.0 4.0388e-03 2.8 1.08e+05 0.0 0.0e+00
>>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 27 79 37
>>> 5.84e-04 68 6.80e-05 100
>>> 271:MGInterp Level 2 68 1.0 2.9033e-02 2.9 1.25e+06 1.9 1.6e+03
>>> 7.8e+02 0.0e+00 0 0 1 0 0 5 0 2 0 0 812 11539 36
>>> 1.14e-01 136 5.41e-02 100
>>> 274:MGInterp Level 3 68 1.0 4.9503e-02 1.1 2.50e+07 1.4 1.1e+04
>>> 6.3e+02 0.0e+00 0 0 7 0 0 9 1 13 1 0 11476 100889 36
>>> 2.29e+00 136 3.74e-01 100
>>> 277:MGInterp Level 4 68 1.0 1.7674e-01 1.0 3.23e+08 1.2 6.1e+03
>>> 6.7e+03 0.0e+00 0 5 4 1 0 33 11 7 4 0 42715 621223 36
>>> 2.98e+01 136 3.95e+00 100
>>>
>>> ex56-summit-gpu-36ranks-converged.txt
>>> 236:MatMultTranspose 136 1.0 2.9692e-01 1.0 1.17e+08 1.2 1.9e+04
>>> 1.5e+03 0.0e+00 1 3 6 1 0 40 6 10 3 0 13521 336701 8
>>> 2.08e+01 272 1.59e+00 100
>>> 268:MGInterp Level 1 68 1.0 3.8752e-03 2.5 1.03e+05 0.0 0.0e+00
>>> 0.0e+00 0.0e+00 0 0 0 0 0 0 0 0 0 0 27 79 37
>>> 3.95e-04 68 4.53e-05 100
>>> 271:MGInterp Level 2 68 1.0 3.5465e-02 2.2 9.12e+05 2.5 2.4e+03
>>> 7.1e+02 0.0e+00 0 0 1 0 0 4 0 1 0 0 655 5989 36
>>> 8.16e-02 136 4.89e-02 100
>>> 274:MGInterp Level 3 68 1.0 6.7101e-02 1.1 1.75e+07 1.5 2.3e+04
>>> 3.9e+02 0.0e+00 0 0 7 0 0 9 1 12 1 0 8455 56175 36
>>> 1.55e+00 136 3.03e-01 100
>>> 277:MGInterp Level 4 68 1.0 2.4317e-01 1.0 2.20e+08 1.2 1.4e+04
>>> 3.4e+03 0.0e+00 0 5 4 1 0 33 11 7 4 0 31062 586044 36
>>> 1.99e+01 136 2.82e+00 100
>>
>> 223:MatMultTranspose 136 1.0 2.0702e-01 2.9 6.59e+07 1.2 2.7e+04 1.1e+03
>> 0.0e+00 1 3 7 1 0 7 6 12 3 0 19553
>> 251:MGInterp Level 1 68 1.0 2.8062e-04 1.5 9.79e+04 0.0 0.0e+00 0.0e+00
>> 0.0e+00 0 0 0 0 0 0 0 0 0 0 349
>> 254:MGInterp Level 2 68 1.0 6.2506e-0331.9 9.69e+05 0.0 2.1e+03 6.3e+02
>> 0.0e+00 0 0 1 0 0 0 0 1 0 0 3458
>> 257:MGInterp Level 3 68 1.0 4.8159e-02 6.5 9.62e+06 1.5 2.5e+04 4.2e+02
>> 0.0e+00 0 0 6 0 0 1 1 11 1 0 11199
>> 260:MGInterp Level 4 68 1.0 2.5707e-01 1.5 1.23e+08 1.2 2.7e+04 1.9e+03
>> 0.0e+00 1 5 7 1 0 13 12 12 5 0 29294
>>
>> Power9 still has an edge here.
