How about replacing --download-fblaslapack with vendor specific BLAS/LAPACK?
Murat On Mon, Apr 3, 2017 at 2:45 PM, Richard Mills <richardtmi...@gmail.com> wrote: > On Mon, Apr 3, 2017 at 12:24 PM, Zhang, Hong <hongzh...@anl.gov> wrote: > >> >> On Apr 3, 2017, at 1:44 PM, Justin Chang <jychan...@gmail.com> wrote: >> >> Richard, >> >> This is what my job script looks like: >> >> #!/bin/bash >> #SBATCH -N 16 >> #SBATCH -C knl,quad,flat >> #SBATCH -p regular >> #SBATCH -J knlflat1024 >> #SBATCH -L SCRATCH >> #SBATCH -o knlflat1024.o%j >> #SBATCH --mail-type=ALL >> #SBATCH --mail-user=jychan...@gmail.com >> #SBATCH -t 00:20:00 >> >> #run the application: >> cd $SCRATCH/Icesheet >> sbcast --compress=lz4 ./ex48cori /tmp/ex48cori >> srun -n 1024 -c 4 --cpu_bind=cores numactl -p 1 /tmp/ex48cori -M 128 -N >> 128 -P 16 -thi_mat_type baij -pc_type mg -mg_coarse_pc_type gamg -da_refine >> 1 >> >> >> Maybe it is a typo. It should be numactl -m 1. >> > > "-p 1" will also work. "-p" means to "prefer" NUMA node 1 (the MCDRAM), > whereas "-m" means to use only NUMA node 1. In the former case, MCDRAM > will be used for allocations until the available memory there has been > exhausted, and then things will spill over into the DRAM. One would think > that "-m" would be better for doing performance studies, but on systems > where the nodes have swap space enabled, you can get terrible performance > if your code's working set exceeds the size of the MCDRAM, as the system > will obediently obey your wishes to not use the DRAM and go straight to the > swap disk! I assume the Cori nodes don't have swap space, though I could > be wrong. > > >> According to the NERSC info pages, they say to add the "numactl" if using >> flat mode. Previously I tried cache mode but the performance seems to be >> unaffected. >> >> >> Using cache mode should give similar performance as using flat mode with >> the numactl option. But both approaches should be significant faster than >> using flat mode without the numactl option. I usually see over 3X speedup. >> You can also do such comparison to see if the high-bandwidth memory is >> working properly. >> >> I also comparerd 256 haswell nodes vs 256 KNL nodes and haswell is nearly >> 4-5x faster. Though I suspect this drastic change has much to do with the >> initial coarse grid size now being extremely small. >> >> I think you may be right about why you see such a big difference. The > KNL nodes need enough work to be able to use the SIMD lanes effectively. > Also, if your problem gets small enough, then it's going to be able to fit > in the Haswell's L3 cache. Although KNL has MCDRAM and this delivers *a > lot* more memory bandwidth than the DDR4 memory, it will deliver a lot less > bandwidth than the Haswell's L3. > >> I'll give the COPTFLAGS a try and see what happens >> >> >> Make sure to use --with-memalign=64 for data alignment when configuring >> PETSc. >> > > Ah, yes, I forgot that. Thanks for mentioning it, Hong! > > >> The option -xMIC-AVX512 would improve the vectorization performance. But >> it may cause problems for the MPIBAIJ format for some unknown reason. >> MPIAIJ should work fine with this option. >> > > Hmm. Try both, and, if you see worse performance with MPIBAIJ, let us > know and I'll try to figure this out. > > --Richard > > >> >> Hong (Mr.) >> >> Thanks, >> Justin >> >> On Mon, Apr 3, 2017 at 1:36 PM, Richard Mills <richardtmi...@gmail.com> >> wrote: >> >>> Hi Justin, >>> >>> How is the MCDRAM (on-package "high-bandwidth memory") configured for >>> your KNL runs? And if it is in "flat" mode, what are you doing to ensure >>> that you use the MCDRAM? Doing this wrong seems to be one of the most >>> common reasons for unexpected poor performance on KNL. >>> >>> I'm not that familiar with the environment on Cori, but I think that if >>> you are building for KNL, you should add "-xMIC-AVX512" to your compiler >>> flags to explicitly instruct the compiler to use the AVX512 instruction >>> set. I usually use something along the lines of >>> >>> 'COPTFLAGS=-g -O3 -fp-model fast -xMIC-AVX512' >>> >>> (The "-g" just adds symbols, which make the output from performance >>> profiling tools much more useful.) >>> >>> That said, I think that if you are comparing 1024 Haswell cores vs. 1024 >>> KNL cores (so double the number of Haswell nodes), I'm not surprised that >>> the simulations are almost twice as fast using the Haswell nodes. Keep in >>> mind that individual KNL cores are much less powerful than an individual >>> Haswell node. You are also using roughly twice the power footprint (dual >>> socket Haswell node should be roughly equivalent to a KNL node, I >>> believe). How do things look on when you compare equal nodes? >>> >>> Cheers, >>> Richard >>> >>> On Mon, Apr 3, 2017 at 11:13 AM, Justin Chang <jychan...@gmail.com> >>> wrote: >>> >>>> Hi all, >>>> >>>> On NERSC's Cori I have the following configure options for PETSc: >>>> >>>> ./configure --download-fblaslapack --with-cc=cc >>>> --with-clib-autodetect=0 --with-cxx=CC --with-cxxlib-autodetect=0 >>>> --with-debugging=0 --with-fc=ftn --with-fortranlib-autodetect=0 >>>> --with-mpiexec=srun --with-64-bit-indices=1 COPTFLAGS=-O3 CXXOPTFLAGS=-O3 >>>> FOPTFLAGS=-O3 PETSC_ARCH=arch-cori-opt >>>> >>>> Where I swapped out the default Intel programming environment with that >>>> of Cray (e.g., 'module switch PrgEnv-intel/6.0.3 PrgEnv-cray/6.0.3'). I >>>> want to document the performance difference between Cori's Haswell and KNL >>>> processors. >>>> >>>> When I run a PETSc example like SNES ex48 on 1024 cores (32 Haswell and >>>> 16 KNL nodes), the simulations are almost twice as fast on Haswell nodes. >>>> Which leads me to suspect that I am not doing something right for KNL. Does >>>> anyone know what are some "optimal" configure options for running PETSc on >>>> KNL? >>>> >>>> Thanks, >>>> Justin >>>> >>> >>> >> >> >