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Merge pull request EESSI#155 from satishskamath/espresso_lj
Adding LJ test within ESPRESSO
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| # | ||
| # Copyright (C) 2018-2024 The ESPResSo project | ||
| # | ||
| # This file is part of ESPResSo. | ||
| # | ||
| # ESPResSo is free software: you can redistribute it and/or modify | ||
| # it under the terms of the GNU General Public License as published by | ||
| # the Free Software Foundation, either version 3 of the License, or | ||
| # (at your option) any later version. | ||
| # | ||
| # ESPResSo is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| # GNU General Public License for more details. | ||
| # | ||
| # You should have received a copy of the GNU General Public License | ||
| # along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
| # | ||
|
|
||
| import argparse | ||
| import time | ||
| import espressomd | ||
| import numpy as np | ||
|
|
||
| required_features = ["LENNARD_JONES"] | ||
| espressomd.assert_features(required_features) | ||
|
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| parser = argparse.ArgumentParser(description="Benchmark LJ simulations.") | ||
| parser.add_argument("--particles-per-core", metavar="N", action="store", | ||
| type=int, default=2000, required=False, | ||
| help="Number of particles in the simulation box") | ||
| parser.add_argument("--sample-size", metavar="S", action="store", | ||
| type=int, default=30, required=False, | ||
| help="Sample size") | ||
| parser.add_argument("--volume-fraction", metavar="FRAC", action="store", | ||
| type=float, default=0.50, required=False, | ||
| help="Fraction of the simulation box volume occupied by " | ||
| "particles (range: [0.01-0.74], default: 0.50)") | ||
| args = parser.parse_args() | ||
|
|
||
| # process and check arguments | ||
| measurement_steps = 100 | ||
| if args.particles_per_core < 16000: | ||
| measurement_steps = 200 | ||
| if args.particles_per_core < 10000: | ||
| measurement_steps = 500 | ||
| if args.particles_per_core < 5000: | ||
| measurement_steps = 1000 | ||
| if args.particles_per_core < 1000: | ||
| measurement_steps = 2000 | ||
| if args.particles_per_core < 600: | ||
| measurement_steps = 4000 | ||
| if args.particles_per_core < 260: | ||
| measurement_steps = 6000 | ||
| assert args.volume_fraction > 0., "volume_fraction must be a positive number" | ||
| assert args.volume_fraction < np.pi / (3. * np.sqrt(2.)), \ | ||
| "volume_fraction exceeds the physical limit of sphere packing (~0.74)" | ||
|
|
||
| # make simulation deterministic | ||
| np.random.seed(42) | ||
|
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|
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| def get_reference_values_per_atom(x): | ||
| # result of a polynomial fit in the range from 0.01 to 0.55 | ||
| energy = 54.2 * x**3 - 23.8 * x**2 + 4.6 * x - 0.09 | ||
| pressure = 377. * x**3 - 149. * x**2 + 32.2 * x - 0.58 | ||
| return energy, pressure | ||
|
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|
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| def get_normalized_values_per_atom(system): | ||
| energy = system.analysis.energy()["non_bonded"] | ||
| pressure = system.analysis.pressure()["non_bonded"] | ||
| N = len(system.part) | ||
| V = system.volume() | ||
| return 2. * energy / N, 2. * pressure * V / N | ||
|
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|
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| system = espressomd.System(box_l=[10., 10., 10.]) | ||
| system.time_step = 0.01 | ||
| system.cell_system.skin = 0.5 | ||
|
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| lj_eps = 1.0 # LJ epsilon | ||
| lj_sig = 1.0 # particle diameter | ||
| lj_cut = lj_sig * 2**(1. / 6.) # cutoff distance | ||
|
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| n_proc = system.cell_system.get_state()["n_nodes"] | ||
| n_part = n_proc * args.particles_per_core | ||
| node_grid = np.array(system.cell_system.node_grid) | ||
| # volume of N spheres with radius r: N * (4/3*pi*r^3) | ||
| box_v = args.particles_per_core * 4. / 3. * \ | ||
| np.pi * (lj_sig / 2.)**3 / args.volume_fraction | ||
| # box_v = (x * n) * x * x for a column | ||
| system.box_l = float((box_v)**(1. / 3.)) * node_grid | ||
| assert np.abs(n_part * 4. / 3. * np.pi * (lj_sig / 2.)**3 / np.prod(system.box_l) - args.volume_fraction) < 0.1 | ||
|
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| system.non_bonded_inter[0, 0].lennard_jones.set_params( | ||
| epsilon=lj_eps, sigma=lj_sig, cutoff=lj_cut, shift="auto") | ||
|
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| system.part.add(pos=np.random.random((n_part, 3)) * system.box_l) | ||
|
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| # energy minimization | ||
| max_steps = 1000 | ||
| # particle forces for volume fractions between 0.1 and 0.5 follow a polynomial | ||
| target_f_max = 20. * args.volume_fraction**2 | ||
| system.integrator.set_steepest_descent( | ||
| f_max=target_f_max, gamma=0.001, max_displacement=0.01 * lj_sig) | ||
| n_steps = system.integrator.run(max_steps) | ||
| assert n_steps < max_steps, f'''energy minimization failed: \ | ||
| E = {system.analysis.energy()["total"] / len(system.part):.3g} per particle, \ | ||
| f_max = {np.max(np.linalg.norm(system.part.all().f, axis=1)):.2g}, \ | ||
| target f_max = {target_f_max:.2g}''' | ||
|
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||
| # warmup | ||
| system.integrator.set_vv() | ||
| system.thermostat.set_langevin(kT=1.0, gamma=1.0, seed=42) | ||
|
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||
| # tuning and equilibration | ||
| min_skin = 0.2 | ||
| max_skin = 1.0 | ||
| print("Tune skin: {:.3f}".format(system.cell_system.tune_skin( | ||
| min_skin=min_skin, max_skin=max_skin, tol=0.05, int_steps=100))) | ||
| print("Equilibration") | ||
| system.integrator.run(min(5 * measurement_steps, 60000)) | ||
| print("Tune skin: {:.3f}".format(system.cell_system.tune_skin( | ||
| min_skin=min_skin, max_skin=max_skin, tol=0.05, int_steps=100))) | ||
| print("Equilibration") | ||
| system.integrator.run(min(10 * measurement_steps, 60000)) | ||
|
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||
| print("Sampling runtime...") | ||
| timings = [] | ||
| energies = [] | ||
| pressures = [] | ||
| for i in range(args.sample_size): | ||
| tick = time.time() | ||
| system.integrator.run(measurement_steps) | ||
| tock = time.time() | ||
| t = (tock - tick) / measurement_steps | ||
| timings.append(t) | ||
| energy, pressure = get_normalized_values_per_atom(system) | ||
| energies.append(energy) | ||
| pressures.append(pressure) | ||
|
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| sim_energy = np.mean(energies) | ||
| sim_pressure = np.mean(pressures) | ||
| ref_energy, ref_pressure = get_reference_values_per_atom(args.volume_fraction) | ||
|
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| print("Algorithm executed. \n") | ||
| np.testing.assert_allclose(sim_energy, ref_energy, atol=0., rtol=0.1) | ||
| np.testing.assert_allclose(sim_pressure, ref_pressure, atol=0., rtol=0.1) | ||
|
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| print("Final convergence met with relative tolerances: \n\ | ||
| sim_energy: ", 0.1, "\n\ | ||
| sim_pressure: ", 0.1, "\n") | ||
|
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| header = '"mode","cores","mpi.x","mpi.y","mpi.z","particles","volume_fraction","mean","std"' | ||
| report = f'''"weak scaling",{n_proc},{node_grid[0]},{node_grid[1]},\ | ||
| {node_grid[2]},{len(system.part)},{args.volume_fraction:.4f},\ | ||
| {np.mean(timings):.3e},{np.std(timings,ddof=1):.3e}''' | ||
| print(header) | ||
| print(report) | ||
| print(f"Performance: {np.mean(timings):.3e}") |