MSU Nanotechnology Education and Research Center...

ATOMISTIC SIMULATIONS ON ATOMISTIC SIMULATIONS ON SUPERCOMPUTERS USINGSUPERCOMPUTERS USING

OPEN SOURCE CODESOPEN SOURCE CODES

Joint Institute for High TemperaturesJoint Institute for High Temperaturesof Russian Academy of Sciencesof Russian Academy of Sciences

Igor MorozovIgor Morozov

MSU Nanotechnology Education and Research CenterInternational School “Computer simulation of advanced materials”, 16–21 July 2012

July 19, 2012

Contents1. Atomistic simulations in material science:

- Molecular Dynamics (MD)- Monte-Carlo (MC)

2. Choice of the simulation software:open source vs. commercial

3. Challenges for atomistic simulation codes:- Interaction models- Parallel scaling- Using GPUs- Atomistic simulations on the Grid

First papers on MD simulations

The record numbers of particles taken for MD simulations in different years

4 nm

1964 1.000 Rahman1984 200.000 Abraham1990 1.000.000 Swope, Anderson1994 100.000.000 Beazley, Lomdahl1997 1.213.857.792 Stadler1997 1.399.440.000 Müller1999 5.180.116.000 Roth2000 8.500.000.000 Vashishta2003 19.000.416.964 Kadau, Germann, Lomdahl2005 160.000.000.000 Kadau, Germann, Lomdahl2006 320.000.000.000 Kadau, Germann, Lomdahl2008 1.000.000.000.000 Kadau, Germann

4 m

Mflops 1964 . CDC 6600 10 MHz 1 CPUsGflops 1985 . Cray 2 125 MHz 8 CPUsTflops 1997 . ASCI Red 200 MHz 9152 CPUsPflops 2008 . Roadrunner 3,2 GHz 122400 CoresEflops 2018 . 108 – 109

Years, Flop/s and the numbers of coresYears, Flop/s and the numbers of cores

Voevodin V.V., XII International supercomputing conference «Scientific Service in the Internet: Supercomputing Centers and Challenges», September 21, 2010.

4 nm

1964 1.000 Rahman1984 200.000 Abraham1990 1.000.000 Swope, Anderson1994 100.000.000 Beazley, Lomdahl1997 1.213.857.792 Stadler1997 1.399.440.000 Müller1999 5.180.116.000 Roth2000 8.500.000.000 Vashishta2003 19.000.416.964 Kadau, Germann, Lomdahl2005 160.000.000.000 Kadau, Germann, Lomdahl2006 320.000.000.000 Kadau, Germann, Lomdahl2008 1.000.000.000.000 Kadau, Germann

4 m

The record numbers of particles taken for MD simulations in different years

Multiscale approach in material sciences

Original at Berkeley Lab We site: http://www.lbl.gov/CS/html/exascale4energy/nuclear.html

Choice of the simulation code

Open sourceFree of charge Allows modificationsImplements cutting edge technologiesTested by many users over the internetSupported by a community of developers

ProprietaryReliableEasier to install and configureProfessional supportUsually has better support for different platforms

Creating new code on the basis of an open source project

Basic simulation algorithmsMD, MC, energy minimization, thermostats, boundary conditions, neighbor lists, optimized electrostatics

Particle interaction modelsForce fields for pair and many-body potentials, customization

Trajectory analysis toolsBinary trajectory output, plausible modules for calculation of thermodynamics, correlation functions, etc. including averaging

Parameter input and variationParameter input languages, batch runs, workflows

Parallel executionParallel efficiency on modern supercomputing clusters

Optimization for a special hardware, GPUVisualization

Key features of atomistic simulation codes

Open source packages for atomistic simulations

-

+

+

++

++

+

GPU

MD for metals and ceramics+++pthreadsCXMDSimple MD, QM/MM, molecular design+++OpenMPFortranTINKER

Coarse-grained models of proteins and nucleic acids

+++C/C++RedMD

High parallel scalability, designed for biomolecular, visualization (VMD)

MPIC++NAMD/VMD

Basic algorithms and force fields, Replica exchange, coarse-grained models, Tcl GUI

++MPIFortran, TclMOILParallel MD for AMBER force field+MPIFortranMDynaMix

High parallel scalability, wide range of potentials and analysis tools

+++MPIC++LAMMPS

General-purpose MD highly optimized for GPUs

++C++, PythonHOOMD-blue

High-precision MD for the large-scale simulation of simple and complex liquids, HDF5 output

+C++HALMD

High performance MD, designed for biological systems and polymers

+MPICGROMACS

General purpose MD, HDF5 output, Java GUI

+Fortran, C++, Java

DL_POLY*

User specified force field (FFML), QM/MM calculations with Empirical Valence Bond (EVB)

++CAdun

CommentMCMDMinParallelLanguagePackage name

SIMULATIONSIMULATIONALGORITHMSALGORITHMS

Molecular Dynamics: Trajectory CalculationSet initial coordinates and positions

Calculate forces between particles

Perform MD step solving the equations of motion

Calculate thermodynamical parameters.Check energy conservation.Output to the trajectory file.

0 0 0 0, , 1,...i i i ir t r v t v i N

i

Ni r

rrUF ),..,( 1

1 1,i n i n i n i nr t r t v t v t

?1 MAXn tt Exitno yes

Monte-Carlo: Metropolis algorithmSet initial coordinates and positions

Randomly displace particles

Calculate the potential energy change

Accept with the given probability:

0 0 0 0, , 1,...i i i ir t r v t v i N

1 1 1 1( , , ),n n n n ni NU U r r U U U

MAXn n Exitno yes

1max max, [0, ], ( ) 1n n

i i i ir r

0 10 exp

UU U

the move is accepted

Return to the previous state

Next step

Equations of motion in Classical MD

1

0 0

1''( ) ( , ,..., ), 1, ,

(0) , (0) .

k k Nk

r t F t r r k Nm

R R V V( )( )kk

U RF Rr

( ) ( ) ( , ) ( , ) .., , , .extk i j i j k

k i j i j kkU R U r U r r rt v r r

1{ ,..., }NR r r 1{ ,..., }NV v vConsider a system of N particles with positions

and velocities .

The Newton’s equations of motion:

Force acting of k-th particle:

General form of the interaction potential:

( )( )

k

kj kj kjpairkj

j jkj kj kj

r U r rF f r

r r r( , ) ( ) ( )i j i j ijU r r U r r U r

Particular case of the pairwise potential:

INTERACTION MODELSINTERACTION MODELS

Interaction models and simulation techniquesParticle-in-cell, hydrodynamic codes

Coarse-grained, discontinuous molecular dynamics

Simple pairwise potentials for atoms ormolecules with fixed atomic bonds

Complicated many-body potentials for atoms (EAM, MEAM, ReaxFF, Tersoff, etc.)

Classical MD for electrons and ions

Wave Packet MD, Electron Force Field

QM/MM hybrid models

Quantum MD (Car-Parinello, DFT, TD-DFT)

Numerical solution of the Schrödinger equation

System size

< 1nm

> 1 mA

tomistic

simulations

Particle-in-cell, hydrodynamic codes






QM/MM hybrid models



System size

< 1nm

> 1 m

Interaction models and simulation techniquesA

tomistic

simulations

Force field for interaction of atoms in a molecule

))cos(1(2

)(2

~)(

2

44),...,(

20,

20,

1 1 0

612

1

nVkllk

rqq

rrrrU

torsions

n

bonds anglesii

iii

i

N

i

N

ij ij

ji

ij

ij

ij

ijijN

Non-bonded interactions(van der Waals)

+

+

-

Non-bonded interactions(electrostatic)

Bond stretching

Angle bendingBond rotation

(torsion)

Source: Pingwen Zhang, Molecular Dynamics Simulations (http://math.xtu.edu.cn/myphp/math/image/MD.ppt)

Andrey G. Kalinichev, Seminar talk,JIHT RAS, Moscow, Russia, October 1, 2009

Guillot B (2002) What we have learnt during three decades of computer simulations on water. Journal of Molecular Liquids 101, 219-260.

Finney JL (2004) Water? What's so special about it? Phil. Trans. R. Soc. Lond. B 359, 1145-1165.

Classical Molecular Models of Water

Andrey G. Kalinichev, Seminar talk,JIHT RAS, Moscow, Russia, October 1, 2009

The Structure of H2O Molecule and Classical Intermolecular Potentials

Ab-initio quantum mechanicalEmpirical and semi-empiricalRigid vs flexiblePoint polarizability“Charges on springs” modelsCore-shell models

U Aij/rij12 - Bij/rij

6 + qiqj / rij) +Short-range repulsion Van der Waals Coulombic

+ ½ kb(rij - r0)2 + ½ k ( ij - 0)2

bond stretching bond bending

SPCSPC/ETIP3P

MCYTIPS2TIP4P

BNSST2TIP5P




Complicated many-body potentials for atoms (Tersoff, EAM, MEAM, ReaxFF, etc.)



QM/MM hybrid models



System size

< 1nm

> 1 m


tomistic

simulations

N

iji j

E U

1 2e e ( )ij ijr rij ij c ijU A B f r

/0 ,ijz b

ijB B e

cos

,

( ) ,( )

ijk

ndik

ijk i j ij

w rz c ew r

2( ) ( ) .rcw r f r e

1.5 2 2.5 3 3.5 4

0

5

10

15 E, eV

r12, Å

r12

Many-body Tersoff potential

Parameters for Si:(J. Tersoff, Phys. Rev. B, 37 (1988) 6991)

1.5 2 2.5 3 3.5 4

0

5

10

15N

iji j

E U

1 2e e ( )ij ijr rij ij c ijU A B f r

/0 ,ijz b

ijB B e

cos

,

( ) ,( )

ijk

ndik

ijk i j ij

w rz c ew r

2( ) ( ) .rcw r f r e

E, eV

r12, Å

r12

r13

Many-body Tersoff potential

Parameters for Si:(J. Tersoff, Phys. Rev. B, 37 (1988) 6991)

i j ij ij iji j i i j

U F r r

Embedded Atom Method - EAM

Pair potential

Electron density (transfer function)

Embedding function

r

(r)

(rij)

( )iji j i

U r?

(r)

i j ij ij iji j i i j

U F r r

Pair potential

Electron density (transfer function)

Embedding function

(r)F( )

Embedded Atom Method - EAM

Atomistic model of spallation processat high rate shock*

Vimp Vsur

impactor target

Model parameters:1. = 300 2. Number of Cu atoms 112 0003. Impactor / target mass ratio 1/64. Initial defect number density 0.05

* A. Yu. Kuksin, G.E.Norman, V. V. Stegailov, and A. V. Yanilkin // Journal of Engineering Thermophysics, 2009, Vol. 18, No. 3, pp. 197–226.

Shock wave in Al crystal with Cu precipitates

Vp = 1800 m/sN = 5 million atoms T=300K

Cu precipitatespiston

Vp

atomsof defects

thin slice

200x80x80


Source: http://lammps.sandia.gov/workshops/Feb10/Aidan_Thompson/ReaxFF_LAMMPS_2010.pdf







QM/MM hybrid models



System size

< 1nm

> 1 m


tomistic

simulations

Density-Temperature Diagram

100 101 102 103 104 105 106 107 108108

1010

10121014

1016

1018

1020

10221024

1026

1028100 101 102 103 104 105 106 107 108

1081010101210141016101810201022102410261028

Classical gas

Quantum gas

Classical nonideal

plasma

Te, K

ne, cm-3

Degenerate nonideal

plasma

Fae /2

110DN

1

/F kT

1/3 243

en ekT

Nonideality parameter for

electrons

343

DD e

rN n

Number of electrons in the Debye sphere

Degeneracy parameter

MDMD

ionization

Breaking into the atom

Source: Alexey Shaitan, International School “Computer simulation of advanced materials”, Moscow, Russia, July 16–21, 2012

Approximations, restrictions and gains

1. BO approx. (adiabatic approx.)No nonadiabatic processes (charge-transfer reactions,

photochemistry)2. Ground state

No electronic excitations, no electron-phonon coupling, etc

3. Classical approx. for nuclei movementNo proton transfer, atom tunneling, etc

On the good side – big systems, long evolution times, nice parallelization and scaling

TD Schrödinger eq. – 3 atomsMD – 10^9 atoms, up to milliseconds

Ionization and cluster expansion «Coulomb explosion»

In the initial stateIn the initial state the ions are located the ions are located in the nodes of Na55 in the nodes of Na55 icosahedralicosahedral crystal. crystal. Electrons are on top of ions.Electrons are on top of ions.

IonIon--electron mass ratio iselectron mass ratio is 4191041910

ErfErf--like electron ion potentiallike electron ion potential is usedis usedwith with UUminmin = = -- 5.1eV 5.1eV

Typical times of:Typical times of:cluster expansioncluster expansion >> 100100fsfselectron oscillationselectron oscillations << 11fsfs

MD simulations of ionized sodium clusters

0 100 200 3000

0.4

0.8

1.2

1.6

t, fs

rrms, nm

Laser heating

Frozen ionst0 = 100fs

t0 = 200fs

2

1

53

iN

rms ii

r r

Cluster expansion

laser pulse: I = 5·1012 W/cm2, = 50 fs

trajectories of ions

electrons

N = 128= 1.28

M/m = 10

An electron is marked as bound if it makes at least one complete revolution around an ion.

– ion

– electron

– bound ion

– bound electron

Visualization of particle motion in MD cell

0.1 0.2 0.3 0.4 0.5 0.6

8

6

4

2

0Coulomb

Deutsch

Cut-off

Error function

Kelbg

r1

kTV

2ekTr

mkTthie

ei

rr

exp11

ei

rr

erf1

ei

rFr1

))erf(1()exp(1)( 2 xxxxF mkTie 2

1,;1,1 rrr

Models for electron-ion interaction potentials

0.1 0.2 0.3 0.4 0.5 0.6

8

6

4

2

0Coulomb

Deutsch

Cut-off

Error function

Corrected Kelbg

r1

kTV

2ekTr

mkTthie

ei

rr

exp11

ei

rr

erf1

))erf(1()exp(1)( 2 xxxxF

22

2 exp~1

eieiei

ei

rkT

eAekTrrF

r

mkTie 2

1,;1,1 rrr

Models for electron-ion interaction potentials







QM/MM hybrid models



System size

< 1nm

> 1 m


tomistic

simulations

)()(24

3exp2

3),( 22

4/3

2 rxprxx iipt

“Physical” parameters (8 real numbers per particle): (r, p) particle coordinate and momentum (3D vectors)

width of the packet ( > 0)p “momentum” of the width

Gaussian wave packet (WP) for a single particle:

r

p

Wave Packet Molecular Dynamics

),()},({ tt kk

k xx

22 2

2 2 2

9ˆ erf2 2 8 2( ) / 3

kk k l klext

k k k lk kl k l

e e rH H Hm m m r

pp

( ) , ( )) , ,( ( )k

k

k kk k

kk

H H HHt t tt pp

r pp r

,dq HNdt q

0 00 0

0 00

0 11 0

0 11 00

N

Wave Packet Molecular DynamicsHamiltonian

Norm-matrix

Equations of motion

2 2

,

ˆ ˆ ˆ ˆ ˆ ˆˆ ˆ ˆ2

ie ei ee ext k ext

k k i k mk i k m

eq eH K V V H Hm x R x x

Many-electron wave function

Total energy

** ln ( ) ( )N

q qq q

(Hartree approximation)

0 1 2 3 4 5 6 7

-80

-70

-60

-50

Eortho, exp = –59.4eV

Epara, exp = –78.98eV

Epara, HF = –77.87eV

Nwp

1 2 3 4 5 6 70.0001

0.001

0.01

0.1

1

Nwp

Ground state of simple atoms represented by multiple Gaussian wave packets

-2 -1 0 1 20

1

2

1

x

2

3

Hydrogen Helium

Excited states of Hydrogen represented by multiple Gaussian wave packets

-12 -8 -4 0 4

I(E)

E, eV

E0 E1

0.35 0.36 0.37 0.38 0.39 0.4 0.410

0.01

0.02

0.03

0.04Eabs, a.u.

, a.u.

Lyman-

Absorbed energy depending on the laser frequency in the area

of 1s 2s transitionNwp = 5, T = 200a.u., E0 = 0.005a.u.

Spectrum of the time correlation function after

excitation

Ionization of H(1s) atom by a laser pulse*

* Comparison with J.P. Hansen, J. Lu, L.B. Madsen, H.M. Nilsen // Phys. Rev. A, 2001, V. 64, P. 033418.

bound free

Classical Trajectory Motne-Carlo (CTMC):

0partE

tot

NP

N

WPMD(1 electron, 3 WPs):

free freeP

0.1 1 100.001

0.01

0.1

1 w

Electron force field approach*

* J.T. Su, W.A. Goddard III, Excited Electron Dynamics Modeling ofWarm Dense Matter, Phys. Rev. Lett., 2007, v. 99, p. 185003.

PARALLEL EXECUTIONPARALLEL EXECUTION

LLNL SequoiaSupercomputer

Top-500: System Architecture

Domain decompositionDomain decomposition

rcut

Lr

iLr

cutr

skin

Speedup depending on the number of CPU cores*

ideal

spee

dup

ideal

spee

dup

Number of CPU cores,

t1/tM t1/tMNumber of particles

Lennard-Jones EAM, Al

Simulations were performed at the MVS-100K cluster of JSCC RASusing LAMMPS


Number of CPU cores,

Number of particles

The number of particles is fixed toN = 32 000

Lennard-Jones interaction potential

with large cut-off lengthRcut = 7.0

Computing clusterMIPT–60

ApplicationLAMMPS

CPU number limit due tointerprocess communications


1 10 100 10000.1

1.0

10.0

100.0

1000.0 Computational time, s

Number of CPU cores

Total timeComputation of forcesCommunications

NAMD: weak scaling for different clusters

Source: http://www.ks.uiuc.edu/Research/namd/performance.html

ACCELERATION USING ACCELERATION USING GPUsGPUs

Top 500 Supercomputers (June 2012)*

* http://www.top500.org

IBM

2011 IBM Corporation60

Accelerators in supercomputers

: Top500.org., XIII

: », 14-24 2011 .

*Anton Dzhoraev, The 1st Research and Practice Conference “High Performance Computing on GPUs”, Perm, Russia, May 21-25, 2012

GPU vs CPU:Performance, Cost, Energy Saving*

GFlops/$K GFlops/KWLinpack performance, GFlops

*Original location: http://www.chepr.ru/index.php?rasd=info&id=75

Comparison of CPU and GPU architectures*

Highly Optimized Object Oriented Molecular DynamicsDevelopers: J.A. Anderson, A. TravessetIowa State University, Ames, IA 50011, USAhttp://codeblue.umich.edu/hoomd-blue/, Free.

Large-scale Atomic/Molecular Massively Parallel SimulatorSandia National Lab, USAhttp://lammps.sandia.gov, Free.

NAMD/VMDUniversity of Illinois at Urbana-Champaign (UIUC), USAhttp://www.ks.uiuc.edu/Research/namd, Free.

MDGPUMDGPU

LAMMPSLAMMPS

ACEACE--MD MD AMBERAMBER

GPU-enabled MD simulation packages

HALMDHALMD+

Benchmarks for Lennard-Jones

liquid:= 0.84, T=0.64,

rcut = 3, rskin = 0.8

103 104 105 106

100

101

102

103

104

105

HOOMD E5520, 1coreLAMMPS E5520, 1coreLAMMPS E5520, 8coresLAMMPS GTX480HOOMD GTX480

Time per step, ms

NCompilers:

icc 11.0 20090318CUDA 3.2, V0.2.1221

MD simulation packages:

LAMMPS (18 Feb 2011)HOOMD ver. 0.9.1

Time of a single step execution (the lower the better)for different number of particles

6060 XX

Benchmarks for Lennard-Jones

liquid:= 0.19, T=1.0,

rcut = 3, rskin = 0.8

Compilers:icc 11.0 20090318

CUDA 3.2, V0.2.1221


LAMMPS (20 May 2010)HOOMD ver. 0.9.0

103 104 105 106

0

20

40

60

GPU GTX480/CPU E5520(1core)GPU GTX480/CPU E5520 - LAMMPSGPU C2050/CPU E5520(1core)GPU C1060/CPU E5520(1core)

PGPU /PCPU

N

Ratio between GPU and CPU performancesdepending on the number of particles

Benchmarks forLJ liquid:

= 0.19, T=1.0,rcut = 3, rskin = 0.8


CUDA 3.2, V0.2.1221


LAMMPS (5 Apr 2011)

1 10 100

102

103

CPU X5670GPU C2050ideal efficiencyP = Ncores /(a + bNcores)ideal efficiency

Steps per second, s-1

Ncores

Npart = 16·103

Time of a single step execution for different number of CPU/GPU cores of the hybrid cluster

Cluster:K100 (KIAM RAS)

Npart = 4·106

1 10 10010-1

100

101

102

CPU X5670GPU C2050ideal efficiencyideal efficiencyP/P1 = 80%P/P1 = 62%

Steps per second, s-1

Ncores

Time of a single step execution for different number of CPU/GPU cores of the hybrid cluster

Benchmarks forLJ liquid:

= 0.19, T=1.0,rcut = 3, rskin = 0.8


CUDA 3.2, V0.2.1221


LAMMPS (5 Apr 2011)

Cluster:K100 (KIAM RAS)

11000 X0 XGPU:

NVIDIA Tesla M2050CUDA 4.0, V0.2.1221

CPU:Xeon E5520 (Nehalem),

2.27GHz, 8Mb L3Intel compilerver. 11.0.083

Interactions:Coulomb

(long-range)100 1000 10000 100000

1

10

100PGPU /PCPU

N

Ratio between GPU and CPU performancesdepending on the number of particles

RIKEN RIKEN ““Protein ExplorerProtein Explorer””: : special purpose system for molecular dynamicsspecial purpose system for molecular dynamics

1 unit = Dual-core Intel Xeon CPU+ 24 MD-GRAPE chips

201 units achieve 1 PFlops for MD

ATOMISTIC SIMULATIONSATOMISTIC SIMULATIONSON THE GRIDON THE GRID

MD (or MC) numerical experiment usually goes through a number of stages:

Low level: • prepare system in the initial state• propagate it through the chain of other states• obtain system properties from the MD trajectory

High level: • average over a set of initial states• perform parameter sweep• perform optimal parameter searchetc

need for predefined workflow scenarios

need for workflow

Requirements to Grid middleware

production

Init

Systemcycle

Finalize

logical

Scenarios and workflow graphs

s0

s1

s2

s3

s4

A2 B2

A2 B1

A1 B2

A1 B1

select parameter A

select parameter B

Graph elements Scenario examples

• Automatic workflow generation from GridMD function calls inside the main application• Component architecture, easy interfacing to other MD packages.

• All required components for MD experiment: boundary conditions, thermodynamic ensembles, etc.)

• Support for various job managers• Cross-platform design, compiled for Linux and Windows

• Open source code (available at http://gridmd.sourceforge.net)

Open source GridMD library

GridMD is a C++ class library intended for constructing simulation applications and running them in distributed environments• Single application for serial (debug) and distributed execution

Batch system (PBS, ...)

Workingnode

Cluster

local shell, ssh, Globus, etc

Workingnode

Workingnode

Workingnode

User codeUser code

MPI

Submission of the tasks to parallel and distributed systems

Simulation program

Grid Middleware

Cluster

local shell, ssh,Globus, etc

User codeUser code

MPI

User codeUser code

MPI

User codeUser code

MPI

i = 1 i = 2 i = N

Workingnode

PBS Cluster

Workingnode

PBS Cluster

Workingnode

PBS


Grid Middleware

Cluster

User codeUser code

GridMD

Workingnode

PBS Cluster

Workingnode

PBS Cluster

Workingnode

PBS

local shell, ssh, Globus, etc

MPI

GridMDapplication


User codeUser codeparameter sweep, optimization, workflows

GridMDworkflow scenarios, internal job manager

MPI

Nimrod Nimrod workflowsworkflows

PBSPBS

DEISA ShellDEISA Shell JSCC RASJSCC RAS

local shell,ssh,

Globus, etc


GridMDapplication

Open source packages for atomistic simulations

-

+

+

++

++

+

GPU

MD for metals and ceramics+++pthreadsCXMDSimple MD, QM/MM, molecular design+++OpenMPFortranTINKER

Coarse-grained models of proteins and nucleic acids

+++C/C++RedMD

High parallel scalability, designed for biomolecular, visualization (VMD)

MPIC++NAMD/VMD

Basic algorithms and force fields, Replica exchange, coarse-grained models, Tcl GUI

++MPIFortran, TclMOILParallel MD for AMBER force field+MPIFortranMDynaMix

High parallel scalability, wide range of potentials and analysis tools

+++MPIC++LAMMPS

General-purpose MD highly optimized for GPUs

++C++, PythonHOOMD-blue

High-precision MD for the large-scale simulation of simple and complex liquids, HDF5 output

+C++HALMD

High performance MD, designed for biological systems and polymers

+MPICGROMACS

General purpose MD, HDF5 output, Java GUI

+Fortran, C++, Java

DL_POLY*

User specified force field (FFML), QM/MM calculations with Empirical Valence Bond (EVB)

++CAdun

CommentMCMDMinParallelLanguagePackage name

Source: http://www.stfc.ac.uk/cse/resources/pdf/biomolbenchiii.pdf

Benchmarks for bimolecular simulations

H.H. Loeffler, M.D. Winn, Large biomolecularsimulations on HPC platforms: III. AMBER, CHARMM, GROMACS, LAMMPS and NAMD, 15 March 2012

CONCLUSIONSOpen source projects are particularly suitable for scientific research because they are customizable, extensible, implement a variety of ionization models and optimization techniques.

Quasiclassical atomistic simulations of chemical reactions, ionization/recombination processes and electron-ion relaxation are still a challenge.

GPUs can highly accelerate MD simulations in particular for long-ranged interactions.

Averaging and parameter sweep lead allow to run simulations on Grid and Cloud environments.

Our web site: http://www.ihed.ras.ru/norman

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