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cluster:182 [2019/08/12 14:26] hmeij07 [the DPP] |
cluster:182 [2019/08/12 17:47] hmeij07 [Scripts] |
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+ | ==== Amber ==== | ||
+ | |||
+ | The RTX compute node only had one GPU, the other nodes had 4 GPUs. In each run the mpi threads requested equaled the number of GPUs involved. Sample script bottom of page. | ||
+ | |||
+ | * [DPFP] - Double Precision Forces, 64-bit Fixed point Accumulation. | ||
+ | * [SPXP] - Single Precision Forces, Mixed Precision [interger] Accumulation. | ||
+ | * [SPFP] - Single Precision Forces, 64-bit Fixed Point Accumulation. (Default) | ||
+ | |||
+ | |||
+ | ^ ns/ | ||
+ | | DPFP | 5.21| 18.35| | ||
+ | | SXFP | 11.82| | ||
+ | | SFFP | 11.91| | ||
+ | |||
+ | Like last testing outcome, in the SFFP precision mode it is best to run four individual jobs, one per GPU (mpi=1, gpu=1). Best performance is the P100 at 47.64 vs the RTX at 39.69 ns/day per node. The T4 runs about 1/3 as fast and really falters in DPFP precision mode. But in SXFP (experimental) precision mode the T4 makes up in performance. | ||
+ | |||
+ | Can't complain about utilization rates.\\ | ||
+ | Amber mpi=4 gpu=4\\ | ||
+ | |||
+ | [heme@login1 amber16]$ ssh node7 ./ | ||
+ | id, | ||
+ | 0, Tesla P100-PCIE-16GB, | ||
+ | 1, Tesla P100-PCIE-16GB, | ||
+ | 2, Tesla P100-PCIE-16GB, | ||
+ | 3, Tesla P100-PCIE-16GB, | ||
+ | |||
+ | ==== Lammps ==== | ||
+ | |||
+ | Precision for GPU calculations | ||
+ | |||
+ | * [DD] -D_DOUBLE_DOUBLE | ||
+ | * [SD] -D_SINGLE_DOUBLE | ||
+ | * [SS] -D_SINGLE_SINGLE | ||
+ | |||
+ | |||
+ | ^ tau/ | ||
+ | | DD | 856, | ||
+ | | SD | 981, | ||
+ | | SS | 1, | ||
+ | |||
+ | As with Amber, it is best to run one job per GPU to achieve max node performance. Depending on problem set performance can be boosted by requesting more MPI threads. In previous tests the P100 in double_double precision mode achieved 2.7 million tau/day, so these results are surprising. The RTX 6000 does a decent job of keeping up with the P100. | ||
+ | |||
+ | But the T4 shines in this application. The mixed or single precision modes compete well given the T4's price and wattage consumption. | ||
+ | |||
+ | ==== Gromacs ==== | ||
+ | |||
+ | Gromacs was build on each of the nodes locally letting it select the optimal CPU (AVX, SSE) and GPU accelerators. The '' | ||
+ | |||
+ | |||
+ | ^ ns/ | ||
+ | | Mixed | | | 254| | | gpu=1, 01-04 | | ||
+ | | Mixed | | 551| | | 546| gpu=4, 01-04 | | ||
+ | | Mixed | | | | | 650| gpu=4, 01-08 | | ||
+ | | Mixed | | | | | 733| gpu=4, 01-16 | | ||
+ | |||
+ | ==== Scripts ==== | ||
+ | |||
+ | All 3 software applications were compiled within default environment and Cuda 10.1 | ||
+ | |||
+ | Currently Loaded Modules:\\ | ||
+ | 1) GCCcore/ | ||
+ | 2) zlib/ | ||
+ | 3) binutils/ | ||
+ | |||
+ | Follow\\ | ||
+ | https:// | ||
+ | |||
+ | * Amber | ||
+ | |||
+ | < | ||
+ | |||
+ | #!/bin/bash | ||
+ | |||
+ | #SBATCH --nodes=1 | ||
+ | #SBATCH --nodelist=node7 | ||
+ | #SBATCH --job-name=" | ||
+ | #SBATCH --ntasks-per-node=1 | ||
+ | #SBATCH --gres=gpu: | ||
+ | #SBATCH --exclusive | ||
+ | |||
+ | # NSTEP = 40000 | ||
+ | rm -f restrt.1K10 | ||
+ | mpirun --oversubscribe -x LD_LIBRARY_PATH -np 1 \ | ||
+ | -H localhost \ | ||
+ | ~/ | ||
+ | -inf mdinfo.1K10 -x mdcrd.1K10 -r restrt.1K10 -ref inpcrd | ||
+ | |||
+ | </ | ||
+ | |||
+ | * Lammps | ||
+ | |||
+ | < | ||
+ | |||
+ | #!/bin/bash | ||
+ | |||
+ | #SBATCH --nodes=1 | ||
+ | #SBATCH --nodelist=node5 | ||
+ | #SBATCH --job-name=" | ||
+ | #SBATCH --gres=gpu: | ||
+ | #SBATCH --ntasks-per-node=1 | ||
+ | #SBATCH --exclusive | ||
+ | |||
+ | # RTX | ||
+ | mpirun --oversubscribe -x LD_LIBRARY_PATH -np 1 \ | ||
+ | -H localhost \ | ||
+ | ~/ | ||
+ | -in in.colloid > rtx-1:1 | ||
+ | |||
+ | [heme@login1 lammps-5Jun19]$ squeue | ||
+ | JOBID PARTITION | ||
+ | 2239 normal | ||
+ | |||
+ | [heme@login1 lammps-5Jun19]$ ssh node5 ./gpu-info | ||
+ | id, | ||
+ | 0, Quadro RTX 6000, 50, 186 MiB, 24004 MiB, 51 %, 0 % | ||
+ | |||
+ | </ | ||
+ | |||
+ | * Gromacs | ||
+ | |||
+ | < | ||
+ | |||
+ | #!/bin/bash | ||
+ | #SBATCH --nodes=1 | ||
+ | #SBATCH --nodelist=node9 | ||
+ | #SBATCH --job-name=" | ||
+ | #SBATCH --ntasks-per-node=32 | ||
+ | #SBATCH --gres=gpu: | ||
+ | #SBATCH --exclusive | ||
+ | |||
+ | export PATH=$HOME/ | ||
+ | export LD_LIBRARY_PATH=$HOME/ | ||
+ | . $HOME/ | ||
+ | rm -f gpu/??/c* gpu/??/e* gpu/??/s* gpu/??/ | ||
+ | cd gpu | ||
+ | |||
+ | # T4 | ||
+ | #export CUDA_VISIBLE_DEVICES=0123 | ||
+ | |||
+ | mpirun -np 8 gmx_mpi mdrun -maxh 1 -gpu_id 0123 \ | ||
+ | -nsteps 1000000 -multidir 05 06 07 08 \ | ||
+ | -ntmpi 0 -npme 0 -s topol.tpr -ntomp 0 -pin on -nb gpu | ||
+ | |||
+ | </ | ||
+ | |||
+ | And GPU utilization was outstanding. | ||
+ | |||
+ | [heme@login1 gromacs-2018]$ ssh node9 ./ | ||
+ | id, | ||
+ | 0, Tesla T4, 66, 866 MiB, 14213 MiB, 98 %, 9 %\\ | ||
+ | 1, Tesla T4, 67, 866 MiB, 14213 MiB, 98 %, 9 %\\ | ||
+ | 2, Tesla T4, 66, 866 MiB, 14213 MiB, 99 %, 9 %\\ | ||
+ | 3, Tesla T4, 64, 866 MiB, 14213 MiB, 97 %, 9 %\\ | ||
\\ | \\ | ||
**[[cluster: | **[[cluster: |