parallel computing—a route to complexity and reality in material simulations

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Parallel Computing—a route to complexity and reality in material simulations Shiwu Gao Department of Applied Physics Chalmers/Göteborg University

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Parallel Computing—a route to complexity and reality in material simulations. Shiwu Gao Department of Applied Physics Chalmers/Göteborg University. Parallel computing and materials simulations. Water-metal interface. Dynamics of electron excitation/transfer. Biomembrane - PowerPoint PPT Presentation

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Page 1: Parallel Computing—a route to complexity and reality in material simulations

Parallel Computing—a route to complexity and reality in material simulations

Shiwu Gao

Department of Applied Physics

Chalmers/Göteborg University

Page 2: Parallel Computing—a route to complexity and reality in material simulations

Parallel computing and materials simulations

Water-metal interface

Dynamics of electron excitation/transfer

BiomembraneAquaporin water channel in membraneK. Murata et al, Nature, 407, 599 (2002)

Page 3: Parallel Computing—a route to complexity and reality in material simulations

Macroscopic(meter, hour)

Mesoscopic

Kinetics

Energetics

Atomic

Electronic(Å, fs)

Bottom-up approach

Theoretical approach based on:

1) Fundamental laws of physics

2) Computer modeling and simulations

Page 4: Parallel Computing—a route to complexity and reality in material simulations

Density Functional Theory based simulations

2

22

)()(

)()()()(2

Fi

i

iiiXCCoulomb

n

nvnm

rr

rr

Solving the Kohn-Sham Equations for all electorns

Full-potential and pseudopotential methods -Ful-potential methods (FP-LAPW, FP-LMTO) accurate and slow

-Pseudo-potential methods (VASP, CPMD, PWSCF) fast but with uncertainty in pseudopotentials

Page 5: Parallel Computing—a route to complexity and reality in material simulations

Outline • Parallelization of WIEN package

FP-(L)APW method

• Applications - Hydrogen bonding by CH group

- Pressure melting of confined water films

Page 6: Parallel Computing—a route to complexity and reality in material simulations

WIEN97 (T. U. Vienna)

Typical timing (s)

H/Cu(100) p(3x3) 3+2+5layers 29 atoms

Potential

Eigenproblem

Density

Core Electron

Mixing in/out data

+ Accurate + Versatile -- Slow-- larger RAM

Page 7: Parallel Computing—a route to complexity and reality in material simulations

Timing in LAPW1

0

10

20

30

40

50

60

70

80

90

H S Hns Solver

Exact

Iterative

- Large memory needed for H,S

RAM ~ M2

- Time-consuming

H |Ψk>= εkS |Ψk> t ~ M3

For large systems (>30 atoms)

- more than 90 % CPU time

- severval GB RAM

Page 8: Parallel Computing—a route to complexity and reality in material simulations

Parallelizing the eigenproblem (LAPW1)

2. Parallelizing the eigensolver

-Incorporating PQR

-Writting an iterative parallel solver

Myid = 0 1 2 3 0 1 2 3 0

1. Distributing and parallel setting H and S

PQR:X.B. Chi, Inst. Software, Chinese Academy of Sciences,Beijing

Page 9: Parallel Computing—a route to complexity and reality in material simulations

Further Parallelizations

+ LAPW1 Distributing H S setting and parallelizing the eigensolver -Incorporating PQR

-Writting an iterative parallel solver

+ LAPW2 and LAPW0

Distributing the calculation atom-wise

+ Implemeting the new APW+lo basis, E. Sjöstedt, Nordström, and Singh, Solid State Commun 114, 15 (2001)

S. Gao, Comput. Phys. Commun. (to be published)

Page 10: Parallel Computing—a route to complexity and reality in material simulations

Test example: C2H4+O2/Ag(110) coadsorption

- 100 surface atoms -Ag(110) 3x4x7=84

-(C2H4+o2)x2=16

- 6 layer vacuum- 21x23x35 au3

- Dual basis -Ag(110) LAPW -molecules, APW+lo

- 1-k point- 9 Ry cut-off structure- 13 -16 Ry in energy- 12 min/SCF 24 SGI3k- 12-15 Ionic steps/day

Page 11: Parallel Computing—a route to complexity and reality in material simulations

Scaling on IBM SP3 (PDC, KTH)

Tested up to48 CPUs

+ Nearly linear-scaling

M=14400

Page 12: Parallel Computing—a route to complexity and reality in material simulations

Scaling on Seth---Linux cluster at HPC2N

Up to 128 CPUs

Seth and SP3: 1) comparable scaling, 2) different in speed

Page 13: Parallel Computing—a route to complexity and reality in material simulations

Summary on scaling and performance

Timing consuming parts Acceleration on p CPUs

Setting H and S 0.98—1.0 p

HNS 0.79—0.9 p

Eigensolver--PQR 0.91–-0.94 p

Iterative Diag. 0.7— 0.8 p

Charge (LAPW2) ~ Na (or no acc.)

Potential (LAPW0) ~ Na (or no acc.)

Applicable to large systems, as PW-PP methods

Page 14: Parallel Computing—a route to complexity and reality in material simulations

Hydrogen bonding by CH group C2H4+O2/Ag(110)

Expt: J. R. Hahn, W. Ho, UCITheory: S. W. Gao, Chalmers

Page 15: Parallel Computing—a route to complexity and reality in material simulations

Why hydrogen bond with CH group

• H-bond is ubiquetous in biomolecules and organics

• Also of interest for fundamental studies (Ionic, covelency, vdW?)

• Usually with FH (VII), OH (VI), and NH(V) due to the large affinity, favoring ionic coupling

• H-bond with CH, weak—controversial EHB < 1 kcal/mol (c.a. 43 meV)

Page 16: Parallel Computing—a route to complexity and reality in material simulations

Building artificial complex with organics

Page 17: Parallel Computing—a route to complexity and reality in material simulations

Questions:

- Structure and orientations

- Interaction between ethylene oxygen

- IE-STS

Page 18: Parallel Computing—a route to complexity and reality in material simulations

Distance-dependent interaction

-27.4 meV

-90.4 meV

-6.6 meV

In the gas phase: the interaction is negligible ~ + 10 meV

Page 19: Parallel Computing—a route to complexity and reality in material simulations

Mechanism of H-bond formation --adsorption induced electron transfer

Page 20: Parallel Computing—a route to complexity and reality in material simulations

Pressure Melting of Confined Water from ab initio Molecular Dynamics Simulation

Page 21: Parallel Computing—a route to complexity and reality in material simulations

Background and Motivation

• Special phenomena in confined water• Bio-membrane fusion: role of thin water films• Pressure:

-phase control-material synthesis-mechanical stimuli in biology

• Ice-skating and lubrication• How to characterize confined liquid water from computer simulations

Page 22: Parallel Computing—a route to complexity and reality in material simulations

• New water phases in confined water

• Existence of solid-liquid critical points

K.Koga et al.,Nature 412, 802 (2001)

Page 23: Parallel Computing—a route to complexity and reality in material simulations

Bio-membrane Fusion

Science 297, 1817 & 1878 (2002)

Page 24: Parallel Computing—a route to complexity and reality in material simulations

Phase Diagram of Water

Science 297, 1288 (2002)

Page 25: Parallel Computing—a route to complexity and reality in material simulations

Simulation Method

• VASP—Veinna ab intio simulation package (better adapted to MD simulations)

.• Slab representation in a supercell

geometry: up to 48 Pt atoms and 32 H2O molecules

Page 26: Parallel Computing—a route to complexity and reality in material simulations

Applying the pressure

Pt

Water

Pt

ΔZ

Page 27: Parallel Computing—a route to complexity and reality in material simulations

Kinetic Energy vs. ΔZ

Transition atvolume change 6.6 %

Bulk ice (expt.) 6.4 %

Page 28: Parallel Computing—a route to complexity and reality in material simulations

Layer-resolved Ek~ΔZ

4th Layer3rd Layer2nd Layer1st Layer

Page 29: Parallel Computing—a route to complexity and reality in material simulations

Estimating the pressure

P = F / S

Page 30: Parallel Computing—a route to complexity and reality in material simulations

Trajectories of a molecule from the simulation

Before meltingAfter melting

--------

Page 31: Parallel Computing—a route to complexity and reality in material simulations

Solid Ice vs. Liquid Water

Page 32: Parallel Computing—a route to complexity and reality in material simulations

Hydrogen Bond Dynamics

Page 33: Parallel Computing—a route to complexity and reality in material simulations

Pair Correlation Function

Page 34: Parallel Computing—a route to complexity and reality in material simulations

Summary

• Parallel WIEN for large-scale ab initio electron structure calculations

• Applications in material simulations1. Hydrogen bonding mechanism induced by

adsorption 2. Pressure induced phase transitions of water

films

Page 35: Parallel Computing—a route to complexity and reality in material simulations

Route to complexityParallelcomputing

Single CPU processing

Page 36: Parallel Computing—a route to complexity and reality in material simulations

Acknowledgements

• Sheng Meng

• Supported by VR

• E. G. Wang’s (IOP, Beijing), B. Kasemo, Chalmers • P. Blaha (TU Vienna)

• E. Sjöstedt, L. Nordström (Uppsala)

• Technical support from (Ulf Andersson , Niclas Andersson, Torgny Faxen)