large-scale molecular dynamics simulations of materials on parallel computers

Large-Scale Molecular Dynamics Simulations of Materials on Parallel Computers

CCLMSCCLMSCCLMSCCLMS

Aiichiro Nakano & Priya VashishtaConcurrent Computing Laboratory for Materials Simulations

Department of Computer ScienceDepartment of Physics & Astronomy

Louisiana State UniversityEmail: nakano@bit.csc.lsu.edu URL: www.cclms.lsu.edu

VII International Workshop on Advanced Computing & Analysis Techniques in Physics Research

Organizers:Dr. Pushpalatha Bhat & Dr. Matthias Kasemann

October 19, 2000, Fermilab, IL

Outline

1. Scalable atomistic-simulation algorithms

2. Multidisciplinary hybrid-simulation algorithms

3. Large-scale atomistic simulation of nanosystems

> Nanophase & nanocomposite materials

> Nanoindentation & nano-impact damage

> Epitaxial & colloidal quantum dots

4. Ongoing projects

Concurrent Computing Laboratory for Materials Simulations

Faculty (Physics, Computer Science): Rajiv Kalia, Aiichiro Nakano, Priya Vashishta

Postdocs/research faculty: Martina Bachlechner, Tim Campbell, Hideaki Kikuchi, Sanjay Kodiyalam, Elefterios Lidorikis, Fuyuki Shimojo,Laurent Van Brutzel, Phillip Walsh

Ph.D. Students: Gurcan Aral, Paulo Branicio, Jabari Lee, Xinlian Liu, Brent Neal, Cindy Rountree, Xiaotao Su, Satavani Vemparala, Troy Williams

Visitors: Elisabeth Bouchaud (ONERA), Antonio da Silva (São Paulo),Simon de Leeuw (Delft), Ingvar Ebbsjö (Uppsala), Hiroshi Iyetomi (Niigata), Shuji Ogata (Yamaguchi), Jose Rino (São Carlos)

• Ph.D. in physics & MS from computer science in 5 years —Broad career options (APS News, August/September, ‘97)

• Synergism between HPCC (MS) & application (Ph.D.) research—Best dissertation award (Andrey Omeltchenko, ‘97)

—MS publication (Parallel Comput., IEEE CS&E, Comput. Phys. Commun., etc.)

• Internship—deliverable-oriented approach to real-worldproblems provides excellent job training

Boeing, NASA Ames, Argonne Nat’l Lab. (Web-based simulation/

experimentation, Alok Chatterjee, Enrico Fermi Scholar, ‘99)

• International collaborationNiigata, Yamaguchi (NSF/U.S.-Japan), Studsvik (Sweden), Delft (The Netherlands), São Carlos (Brazil)

• NSF Graduate Research Traineeship Program

• New program: Ph.D. biological sciences & MS computer science

Education: Dual-Degree Opportunity

Web-based course involving LSU, Delft Univ. in the Netherlands,Niigata Univ. in Japan, & Federal Univ. of Sao Carlos in Brazil

SPOriginT3E

The NetherlandsDelft Univ.

ImmersaDesk

USALouisiana State Univ.

VR workbench

Video Conferencing

Virtual Classroom

Chat Tool Whiteboard Tool

International Collaborative Course

Alpha cluster

DoD Challenge Applications Award1.3 million node-hours in 2000/2001

1. Scalable Atomistic-Simulation Algorithms

CCLMSCCLMSCCLMSCCLMS

• Peta (1015) flop computers direct atomistic simulations• Scalable applications multiresolution algorithms are key

Teraflop

0.25 m

Atomistic regime

107-109 atoms

CMOS (SIA Roadmap)

Molecular Dynamics

Petaflop

70 nm

Continnum regime

1010-1012 atoms

Atomistic Simulationof Real Devices

10 nm

100 nm

1 m

1996 1998 2000 2002 2004 2006 2008 2010

Lin

e w

idth

Year

Atomistic Simulation of Nanosystems

• Newton’s equations of motion

• Many-body interatomic potential> 2-body: Coulomb; steric

repulsion; charge-dipole; dipole-dipole

> 3-body: Bond bending & stretching

—SiO2, Si3N4, SiC, GaAs, AlAs, InAs, etc.

Molecular Dynamics Simulation

mi

d 2 r idt 2

Vr N

r i

(i 1, ..., N)

V uij rij

i j v jik

r ij ,

r ik

i, jk

riki

jkrij

Validation of Interatomic Potentials

Phonon dispersion

High-pressure phase transition

Si3N4

amorphous

0

10

20

30

40

0

10

20

30

40

Freq

uen

cy (

meV

)

K X L X W L

Experiment (Strauch & Dorner, '90)

Theory

0

0.5

1

1.5

2

0 5 10 15 20

Expt.

MD

SN(q

)

q (Å -1)

Johnson et al. (‘83)

3

4

5

0 50 100 150

Den

sity

(g/

cm3 )

Pressue (GPa)

Reversetransition (Expt.)

MD

Forward transition (Expt.)

Yoshida et al. (‘93)

SiC

amorphous SiO2

Neutron staticstructure factor

GaAs

Space-time Multiresolution AlgorithmChallenge 1: Scalability to billion-atom systems

• Hierarchical Fast Multipole Method

• Multiple Time-Scale method

O(N2) O(N)

Long-range Short-range

FMM MTS

Rapid

Slow

1.02 billion-atom MD for SiO2: 26.4 sec/step on 1,024 Cray T3E processors at NAVO-MSRC, Parallel efficiency = 0.97

Scaled speedupon Cray T3E

Wavelet-based Load Balancing

Irregulardata-structures/processor-speed

Parallelcomputer

Map

“Computational-space decomposition” in curved space

Challenge 2: Load imbalance on a parallel computer

Regular mesh topology in computational space,

Curved partition in physical space, x

Wavelet representation speeds up optimization of (x)

Fractal-based Data Compression

Scalable encoding:• Spacefilling curve—store relative positions

Result:• I/O size, 50 Bytes/atom 6 Bytes/atom

1

98

7

65

4

3

2

14 13

1211

10

Challenge 3: Massive data transfer via OC-3 (155 Mbps)75 GB/frame of data for a 1.5-billion-atom MD!

VES i

0qi 12 Ji

0qi2

i

12

d3r1 d3r2

i r1;qi j r2 ;q j r12

ij

Intra-atomic Inter-atomic

Variable-charge MD

Electronegativity equalization:• Determine atomic charges at every MD step—O(N3)!

(Streitz & Mintmire, ‘94)

• i) Fast multipole method; ii) q(init)(t+t) = q(t) O(N)

Multilevel preconditioned conjugate gradient (MPCG):• Sparse, short-range interaction

matrix as a preconditioner• 20% speed up• Enhanced data locality:

parallel efficiency, 0.93 0.96for 26.5M-atom Al2O3on 64 SP2 nodes

Challenge 4: Complex realism—chemical reactions

Linear-Scaling Quantum-Mechanical Algorithm

• Density functional theory (DFT)(Kohn, ‘98 Nobel Chemistry Prize)—O(CN )O(N3 )

• Pseudopotential (Troullier & Martins, ‘91)

• Higher-order finite-difference (Chelikowsky, Saad, et al., ‘94)

• Multigrid acceleration (Bernholc, et al., ‘96)

• Spatial decomposition

O(N) algorithm (Mauri & Galli, ‘94)

• Unconstrained minimization• Localized orbitals• Parallel efficiency ~ 96% for

a 22,528-atom GaAs systemon 1,024 Cray T3E processors

Challenge 5: Complexity of ab initio QM calculations

On 1,280 IBM SP3 processors:• 8.1-billion-atom MD of SiO2• 140,000-atom DFT of GaAs

Scalable MD/QM Algorithm Suite

Design-space diagram on 1,024 Cray T3E processors

Immersive & Interactive Visualization

Last Challenge: Sequential bottleneck of graphics pipeline

• Octree data structure for fast visibility culling

• Multiresolution & hybrid (atom, texture) rendering

• Parallel preprocessing/predictive prefetch

• Graph-theoretical data mining of topological defects

2. Multidisciplinary Hybrid-Simulation Algorithms

CCLMSCCLMSCCLMSCCLMS

Multiscale SimulationLifetime prediction of safety-critical

micro-electro-mechanical systems (MEMS)

• Engineering mechanics experimentally validated > 1 m• Atomistic simulation possible < 0.1 m

[R. Ritchie, Berkeley]

Bridging the length-scale gap by seamlessly coupling:• Finite-element (FE) calculation based on elasticity;• Atomistic molecular-dynamics (MD) simulation;• Ab initio quantum-mechanical (QM) calculation.

Hybrid QM/MD Algorithm

QM

MD

Handshakeatoms

Additive hybridizationReuse of existing QM & MD codes

Handshake atomsSeamless coupling ofQM & MD systems

MD simulation embeds a QM cluster described by a real-space multigrid-based density functional theory

E EMDsystem EQM

cluster EMDcluster

FE/MD/Tight-binding QM (Abraham, Broughton, Bernstein, Kaxiras, ‘98)

Hybrid MD/FE Algorithm• FE nodes & MD atoms coincide in the handshake region• Additive hybridization

MD

FE

[0 1 1]

[1 1 1]_

HS

_[1 1 1]

[2 1 1]

Oxidation on Si Surface

Dissociation energy of O2 on a Si (111) surface dissipated seamlessly from the QM cluster through the MD regionto the FE region

QM cluster

MD FE

QM O

QM Si Handshake H

MD Si

3. Large-Scale Atomistic Simulation of Nanosystems

CCLMSCCLMSCCLMSCCLMS

Fracture Simulation & Experiment

Microcrackcoalescence

Multiplebranching

Si3N4Ti3Al alloyE. Bouchaud

Graphite GlassK. Ravi-Chandar

Good agreement with experimentsPlane Gc (MD) Gc (expt.)(110) 1.4 ± 0.1 1.72*

1.52#

Fracture Energy of GaAs: 100-million-atom MD Simulation

256 Cray T3E processors at DoD’s NAVO-MSRC

1.3 m

-0.8 0 0.8 Shear stress (GPa)

*Messmer (‘81) #Michot (‘88)

Color code: Si3N4; SiC; SiO2

Si3N4-SiC Fiber Nanocomposite

Fracture surfaces in ceramic-fiber nanocomposites:Toughening mechanisms?

1.5-billion-atom MD on 1,280 IMB SP3 processors at NAVO-MSRC

0.3 m

0

Pressure (GPa)

-5 -2 2 >20105

Nanoindentation on Silicon Nitride Surface

Use Atomic Force Microscope (AFM) tipfor nanomechanical testing of hardness

Highly compressive/tensile local stresses

10 million atom MDat ERDC-MSRC

Indentation Fracture & Amorphization

<1210> Indentation fractureat indenter diagonals

Amorphous pile-upat indenter edges

Anisotropicfracture toughness

<1010>

<0001>

Hypervelocity Impact Damage Design of damage-tolerant spacecraft

Impact graphitization

Diamond impactor

Impact velocity: 8 - 15 km/s

Diamond coating

QuickTime™ and aVideo decompressor

are needed to see this picture.Meteoroid detector onMir Orbitor

Reactive bond-order potential (Brenner, ‘90)

V = 8 km/s

V = 15 km/s

V = 11 km/s

Impact-Velocity Sensitivity

Crossover from quasi-elastic to evaporation at ~ 10 km/s

time

-

Epitaxially Grown Quantum Dots

A. Madhukar (USC)

Substrate-encoded size-reducing epitaxy

GaAs (001) substrate; <100> square mesas

10nm

101

GaAsAlGaAsQDQD

001

AlGaAs

70 nm

Stress Domains in Si3N4/Si Nanopixels

Stress domains in Sidue to an amorphousSi3N4 film

-2GPa 2GPa

27 million atom MD simulation

Stress well in Si with a crystalline Si3N4 film due to lattice mismatch

Si

Si3N4

Colloidal Semiconductor Quantum Dots

17.5 GPa

Multiple domains

Applications

• LED, display

• Pressure synthesis of novel materials

High-pressure structural transformationin a GaAs nanocrystal

22.5 GPa

30 Å

Nucleation at surface

Oxide Growth in an Al Nanoparticle

Oxide thickness saturates at 40 Å after 0.5 ns—Excellent agreement with experiments

Unique metal/ceramic nanocomposite

70 Å 110 Å

Al AlOx

4. Ongoing Projects

CCLMSCCLMSCCLMSCCLMS

Information Grid

Metacomputing collaboration with DoD MSRCs:4-billion-atom MD simulation of 0.35 m fiber composites

http://www.nas.nasa.gov/aboutNAS/Future2.html

Universal access to networked supercomputing

I. Foster & C. Kesselman, The Grid: Blueprint for a New Computating Infrastructure (‘99)

MD Moore’s LawNumber of atoms in MD simulations has doubled:• Every 19 months in the past 36 years for classical MD• Every 13 months in the past 15 years for DFT-MD

A petaflop computer will enable 1012-atom MD & 107-atom QM

CDC3600

1,280 x IBM SP3

QM

FE

MD

Si3N4 AFM Tip

Hybrid Simulation of Functionalized AFMNanodevices to design new biomolecules

Biological Computation &Visualization Center, LSU($3.9M, 2000- )

Large-scale, multiscale simulations ofrealistic nanoscale systems will be possible

in a metacomputing environmentof the Information Grid

Conclusion

Research supported

by

NSF, AFOSR, ARO, USC/LSU MURI, DOE, NASA, DOD Challenge Applications Award

CCLMSCCLMSCCLMSCCLMS