Particle Simulations with HOOMD-blue

Transcription

1 Particle Simulations with HOOMD-blue Joshua A. Anderson S6256 NVIDIA GTC April 7, :00-9:50am

2 HOOMD-blue General purpose particle simulation toolkit Molecular dynamics, hard particle Monte Carlo Executes simulations fast on GPUs (also runs on CPUs) Open source >127 peer-reviewed articles 2

3 History CUDA public beta APS March meeting Paper submitted v0.6: C++ benchmarks only, lj and harmonic bonds only v0.7: Python job scripts v0.8: First external contribution - FENE bonds, Langevin dynamics 3

4 History v1.2: Bounding volume hierarchy neighbor list v1.3: Anisotropic particle integrators, wall potentials, load balancing 2016-?? - v2.0: Hard particle Monte Carlo 4

5 Research using HOOMD Quasicrystal formation Active particles Microspheres Digital alchemy Shape allophiles Hexatic phases 5

6 Molecular dynamics ~r i (t) ~q i (t) ~v i (t) ~! i (t) Compute interactions ~r i (t + t) ~q i (t + t) ~v i (t + t) ~! i (t + t) 6

7 Hard particle Monte Carlo (HPMC) ~r i (A) ~q i (A) Random trial move Accept or reject ~r i (B) ~q i (B) Joshua A Anderson, M Eric Irrgang, Sharon C Glotzer (2016) Scalable Metropolis Monte Carlo for simulation of hard shapes, Computer Physics Communications 7

8 HPMC Shape overlap checks + See GTC 2014 talk 8

9 HPMC block level queue Thread ) Time Initialization Trial move Circumsphere check Overlap check Overlap divergence Early exit divergence Time (0.5 ms total) 9

10 HPMC on P100 10

11 GPU strategy in HOOMD Perform all computations on the GPU (see GTC talks) Keep data on the GPU all the time Run multiple threads per particle and warp-level reduction/scan Auto-tune launch parameters during run time (see GTC 2015 talk) 11

12 Architecture User's python script HOOMD python library matplotlib scipy mpi4py... HOOMD C++ library CUDA MPI Hardware 12

13 Installing binaries with conda 13

14 Compile from source Requirements: C++ compiler Python Numpy Boost C++ library CMake Optional: CUDA MPI git sphinx 14

15 Compile from source 15

16 Example: DPD polymers import hoomd from hoomd import md hoomd.context.initialize(); hoomd.init.read_gsd(filename='polymers.gsd') nl = hoomd.md.nlist.cell() dpd = md.pair.dpd(r_cut=1.0, T=1.0, nlist=nl) nl.reset_exclusions() dpd.pair_coeff.set('a', 'A', A=25.0, gamma = 4.5) dpd.pair_coeff.set('a', 'B', A=80.0, gamma = 4.5) dpd.pair_coeff.set('b', 'B', A=25.0, gamma = 4.5) harmonic = hoomd.md.bond.harmonic() harmonic.bond_coeff.set('polymer', k=330.0, r0=0.84) md.integrate.mode_standard(dt=0.02) md.integrate.nve(group=hoomd.group.all()) hoomd.run(10000) 16

17 Running the example 17

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19 Example: Active matter import hoomd, numpy from hoomd import md hoomd.context.initialize() hoomd.init.read_gsd('active.gsd') all = hoomd.group.all() N = len(all) nl = hoomd.md.nlist.cell() lj = md.pair.lj(r_cut=1.0, nlist=nl) lj.pair_coeff.set('a', 'A', epsilon=1.0, sigma=1.0/2**(1.0/6.0)) activity = [ (((numpy.random.rand(3) - 0.5) * 2.0)) for i in range(n)] for i in range(n): activity[i][2] = 0; activity[i] = tuple(activity[i]) active_force = md.force.active(group=all, seed=123, f_lst=activity, rotation_diff=0.005, orientation_link=false) md.integrate.mode_standard(dt=0.001) bd = md.integrate.brownian(group=all, T=0.0, seed=123, dscale=1.0) hoomd.run(100000) 19

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21 BVH tree overview BVH trees are common in ray tracing HOOMD uses them for neighbor searches (optional) Generate and query on the GPU M. P. Howard, J. A. Anderson, A. Nikoubashman, S. C. Glotzer, and A. Z. Panagiotopoulos, Comput. Phys. Commun., Mar

22 Example: Large and small particles import hoomd, numpy from hoomd import md hoomd.context.initialize() hoomd.init.read_gsd('bigsmall.gsd') nl = md.nlist.tree() lj = md.pair.slj(r_cut=2**(1.0/6.0), nlist=nl) lj.pair_coeff.set('a', 'A', epsilon=1.0, sigma=1.0) md.integrate.mode_standard(dt=0.005) lvn = md.integrate.langevin(group=hoomd.group.all(), T=1.0, seed=123) lvn.set_gamma('a', 1.0) hoomd.run( ) 22

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24 Example: Hard Hexagons import hoomd from hoomd import hpmc hoomd.context.initialize(); hoomd.init.read_gsd(filename='hexagons.gsd') hexagon = [[0.5,0], [0.25, ], [-0.25, ], [-0.5,0], [-0.25, ], [0.25, ]]; mc = hpmc.integrate.convex_polygon(seed=123, d=0.15, a=1); mc.shape_param.set('a', vertices=hexagon); hoomd.run(200) 24

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26 Example: Jupyter notebook 26

27 Additional features Integration: NVE, NVT, NPT, NPH, langevin, brownian, Berendsen, DPD, FIRE minimization Orientational degrees of freedom Pair potentials: CGCMM, DPD, LJ, Gaussian, Mie, Moliere, Morse, Yukawa, ZBL, table Bond: FENE, Harmonic, table, OPLS Anisotropic potentials: Gay-berne, dipole Wall potentials PPPM electrostatics HPMC shapes: convex polygon, simple polygon, convex spheropolygon, convex polyhedron, convex spheropolyhedron, general polyhedron, ellipsoid, differences of spheres, unions of spheres, faceted spheres 27

28 Software engineering Object oriented design Template functors Code review / pull requests Unit testing Validation testing Conda recipes Read the docs 28

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31 Read The Docs 31

32 Acknowledgements HOOMD-blue + HPMC: codeblue.umich.edu/hoomd-blue/ 2.0 will be available soon... Thanks to all HOOMD-blue users and contributors! Research supported by the National Science Foundation, Division of Materials Research Award # DMR