Jed Brown <j...@jedbrown.org> writes: > Fande Kong <fdkong...@gmail.com> 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 <j...@jedbrown.org> 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 <j...@jedbrown.org> writes: >> >>> "Smith, Barry F." <bsm...@mcs.anl.gov> 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.