LAMMPS Performance Benchmark on VSC-1 and VSC-2
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1 LAMMPS Performance Benchmark on VSC-1 and VSC-2 Daniel Tunega and Roland Šolc Institute of Soil Research, University of Natural Resources and Life Sciences VSC meeting, Neusiedl am See, February 27-28, 2012
2 Objectives LAMMPS features LAMMPS benchmark tests Parallel performance issues on VSC-1 and VSC-2 Memory performance VSC-1 VSC-2 comparison
3 LAMMPS features Large-scale Atomic/Molecular Massively Parallel Simulator classical molecular dynamics code for modeling o atomic o polymeric o biological o metallic o granular and coarse-grained o hybrid and mesoscopic systems implemented various force fields ensembles, constrains, boundary conditions, integrators multi-replica models (parallel tempering, ) pre- and post-processing specialized features (e.g. GC MC, peridynamics)
4 LAMMPS features runs on a single processor or in parallel runs efficiently in parallel using MPI technique highly portable C++ easily modified and extended runs on various platforms including GPU (CUDA & OpenCL) open-source code, distributed under GNU Public License developed at Sandia National Laboratories core group: S. Plimpton, A. Thomson & P. Crozier
5 LAMMPS in parallel spatial decomposition techniques to partition the simulation domain into small 3d sub domains, one of which is assigned to each processor for computational efficiency LAMMPS uses neighbor lists to keep track of nearby particles processors communicate and store "ghost" atom information for atoms that border their sub domain CPU δt {Comm} = communication {Bond} {Neigh} {Pair} {Other + Output} Compute bonded terms If needed update the neighbor list Compute short- and longrange interaction terms for energy/forces Loop: i = 1 to N Collect forces, time integration and update positions), adjust T/p, print/write output
6 AMMPS on VSC-1 and VSC-2 VSC-1 (Intel X5550) VSC-2 (Opteron 6132HE) LAMMPS version 20Feb oct2011 C++ compiler opt. flags icc (11.1) -O icc (12.1.2) -O MPI Openmpi Openmpi FFTW fftw fftw-3.3
7 LAMMPS benchmark tests In LAMMPS standard distribution 5 benchmark tests Short-range forces modeled with a cut off distance Chain: Polymer chain melt (coarse-grained, FENE/LJ potential) LJ: Lennard-Jones liquid (LJ pot.) EAM: EAM metallic solid (EAM pot.) Chute: granular chute flow (granular pot.) Long-range forces Rhodopsin: solvated rhodopsin protein (CHARMM) All five tests can be run as Fixed-size (default particles, 100 time steps) Scaled-size the size increases with increasing number of cores
8 LAMMPS benchmark tests our test based on our VSC project: molecular dynamics study of wetting of mineral surfaces (SiO2, clays, FeOOH) FF: CLAYFF for minerals and SPC/E for water θ E = E + E + E CLAYFF = total Coul LJ bond stretch e qiq j σ ij σ ij = + 4ε ij + k r r 4πε 0 i j rij i j r ij r ij ( ) 1 ij 0 2
9 LAMMPS benchmark tests Description Chain LJ Rhodo Our test bead-spring polymer melt of 100-mer chains Atomic fluid Rhodopsin protein in solvated lipid bilayer Kaolinite layer with water droplet of 500 water molecules # of atoms k/64k/128k 6940 FF FENE/LJ LJ CHARMM CLAYFF/SPC/E cutoff 2^(1/6) σ 2.5 σ 10 Å 12 / 40 Å Long-rage N/A N/A PPPM Ewald Ensemble NVE NVE NpT NVT T / p 1 kbt/ε 0 kbt/ε 300 K / 1 atm 300 K Time step τ τ 2 fs 1 fs Run time τ 5000 τ 200 ps 200 ps Output 100 steps at the end 50 steps 50 steps
10 AMMPS VSC-1 scalability 250 Ideal scaling Test benchmark (6940 atoms) Chain benchmark (32000 atoms) LJ benchmark (32000 atoms) 200 Rhodo benchmark (32000 atoms) Rhodo benchmark (64000 atoms) Rhodo benchmark ( atoms) Scalability # of cores
11 AMMPS VSC-2 scalability Scalability Ideal scaling Test benchmark (6940 atoms) Chain benchmark (32000 atoms) LJ benchmark (32000 atoms) Rhodo benchmark (32000 atoms) Rhodo benchmark (64000 atoms) Rhodo benchmark ( atoms) # of cores
12 AMMPS VSC-1 and 2 speed-up VSC-1 Rhodo 32k VSC-1 Rhodo 64k VSC-1 Rhodo 128k time [s] VSC-2 Rhodo 32k VSC-2 Rhodo 64k VSC-2 Rhodo 128k # of cores
13 MMPS VSC-1 and VSC-2 run time [s] Test Chain LJ k 32k # cores VSC-1 VSC-2 ratè VSC-1 VSC-2 rate VSC-1 VSC-2 rate Aver.: 0.47 Aver.: 0.72 Aver.: 0.84 Rhodo 32k 64k 128k # cores VSC-1 VSC-2 rate VSC-1 VSC-2 rate VSC-1 VSC-2 rate Aver.: 0.51 Aver.: 0.51 Aver.: 0.54
14 MPS VSC-1 and VSC-2 parallel efficiency Efficiency / % efficiency >80% for optimal sub-domain size of >1000 particles VSC-1 Rhodo 32k VSC-1 Rhodo 128k VSC-2 Rhodo 32k VSC-2 Rhodo 128k ln2(n) cores
15 MPS VSC-1 and VSC-2 parallel efficiency 120 VSC-1 vs VSC Efficiency / % VSC-1 Rhodo 32k VSC-1 Rhodo 128k VSC-2 Rhodo 32k VSC-2 Rhodo 128k ln2(n) cores ln2(n) cores
![MMPS - memory per processor [MB] Test Chair LJ Rhodopsin 6940 32k 32k 32k 64k 128k # cores VSC-1 VSC-2 VSC-1 VSC-2 VSC-1 VSC-2 VSC- 1 VSC-2 VSC-1 VSC-2 VSC-1 VSC-2 1 91 8 12 124 231 428 4 51 4 4 48](/docs-images/96/128303283/images/16-0.jpg "77 128 8 32 3 2 29 48 78 16 8 11 2 3 2 2 23 27 29 33 48 55 32 7 10 2 3 2 2 12 14 23 27 29 33 64 7 10 2 2 2 2 11 12 12 14 23 27 128 5 6 2 2 2 2 10 11 11 13 12 14 256 4 6 2 2 1 2 9 11 10 11 11 12 512 6")
16 MMPS - memory per processor [MB] Test Chair LJ Rhodopsin k 32k 32k 64k 128k # cores VSC-1 VSC-2 VSC-1 VSC-2 VSC-1 VSC-2 VSC- 1 VSC-2 VSC-1 VSC-2 VSC-1 VSC Rhodopsin 32 k 64 k 128k
17 CONCLUSIONS LAMMPS performance boost depends on model: better parallel efficiency for long-range models LAMMPS shows a good parallel performance, however o parallel efficiency of LAMMPS varies from the size of the benchmark data o performance advantage extends as cluster size increases o LAMMPS scales better to more processors for larger systems LAMMPS runs faster on VSC-1 than on VSC-2 (~0.5 for long-range models, for short-range models)
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