beyond teraflops toward petaflops 2 october 2002 computational science and engineering department...

1 Beyond Teraflops toward Petaflops 2 October 2002

Computational Science and Engineering Department Daresbury Laboratory

Beyond Teraflops toward PetaflopsBeyond Teraflops toward PetaflopsComputational Chemistry:Computational Chemistry:

Challenges and OpportunitiesChallenges and Opportunities

Martyn F. Guest and Paul SherwoodMartyn F. Guest and Paul Sherwood

CCLRC Daresbury LaboratoryCCLRC Daresbury Laboratory

[email protected]@daresbury.ac.uk



OutlineOutline From Gigaflops to Teraflops - Computational Chemistry TodayFrom Gigaflops to Teraflops - Computational Chemistry Today

Migration from replicated to distributed dataMigration from replicated to distributed data Parallel linear algebra (diagonalisation, FFT etc.)Parallel linear algebra (diagonalisation, FFT etc.) Exploiting multiple length and time scalesExploiting multiple length and time scales

From Teraflops to PetaflopsFrom Teraflops to Petaflops Problem scaling and re-formulation - Four DimensionsProblem scaling and re-formulation - Four Dimensions

Time to Solution, Problem Size, Enhanced Sampling, AccuracyTime to Solution, Problem Size, Enhanced Sampling, Accuracy Long Term - New horizons for simulationLong Term - New horizons for simulation

““Simulation of whole systems, and not just system components”Simulation of whole systems, and not just system components” Scientific Challenges in Key Application areasScientific Challenges in Key Application areas

Catalytic Processes, Biomolecular Simulations, Heavy Particle DynamicsCatalytic Processes, Biomolecular Simulations, Heavy Particle Dynamics““Current Status”, “Towards Petaflops”, “New Horizons”Current Status”, “Towards Petaflops”, “New Horizons”

The Software ChallengeThe Software Challenge Recommendations and SummaryRecommendations and Summary



1 2 4 8 16 32 64 128 2561

2

4

8

16

32

64

128

256

CPUs per SMP

SM

Ps

per

Clu

ster

How Today’s codes exploit Today’s HardwareHow Today’s codes exploit Today’s Hardware

10GF

100GF

1TF

10TF

Shared Memory

Shared Memory

Distrib

uted

M

emory

Distrib

uted

M

emory

HPCxHPCx

GaussianGaussian

GAMESS-UKGAMESS-UK

NWChemNWChem

DL_POLY 3DL_POLY 3

CHARMMCHARMM

NAMD NAMD



High-End Computational ChemistryHigh-End Computational ChemistryThe NWChem SoftwareThe NWChem Software

Capabilities Capabilities (Direct, Semi-direct and conventional):(Direct, Semi-direct and conventional): RHF, UHF, ROHFRHF, UHF, ROHF using up to 10,000 basis functions; analytic 1st using up to 10,000 basis functions; analytic 1st

and 2nd derivatives.and 2nd derivatives. DFTDFT with a wide variety of local and non-local XC potentials, using with a wide variety of local and non-local XC potentials, using

up to 10,000 basis functions; analytic 1st and 2nd derivatives.up to 10,000 basis functions; analytic 1st and 2nd derivatives. CASSCFCASSCF; analytic 1st and numerical 2nd derivatives.; analytic 1st and numerical 2nd derivatives. Semi-direct and RI-based MP2Semi-direct and RI-based MP2 calculations for RHF and UHF wave calculations for RHF and UHF wave

functions using up to 3,000 basis functions; analytic 1st derivatives functions using up to 3,000 basis functions; analytic 1st derivatives and numerical 2nd derivatives.and numerical 2nd derivatives.

Coupled cluster, CCSD and CCSD(T)Coupled cluster, CCSD and CCSD(T) using up to 3,000 basis using up to 3,000 basis functions; numerical 1st and 2nd derivatives of the CC energy. functions; numerical 1st and 2nd derivatives of the CC energy.

Classical molecular dynamics and free energy simulations with the forces obtainable from a variety of sources



Memory-driven Approaches: NWChem - DFT (LDA) Memory-driven Approaches: NWChem - DFT (LDA) Performance on the SGI Origin 3800Performance on the SGI Origin 3800

• DZVP Basis (DZV_A2) and DgaussDZVP Basis (DZV_A2) and Dgauss A1_DFT Fitting basis: A1_DFT Fitting basis:

AO basis: AO basis: 3554 3554 CD basis:CD basis: 1271312713

• MIPS R14k-500 CPUs (Teras)MIPS R14k-500 CPUs (Teras)

Wall time (13 SCF iterations):Wall time (13 SCF iterations):64 CPUs = 5,242 seconds64 CPUs = 5,242 seconds128 CPUs= 3,451 seconds128 CPUs= 3,451 seconds

Est. time on 32 CPUs = Est. time on 32 CPUs = 40,00040,000 secs secs

Zeolite ZSM-5Zeolite ZSM-5

• 3-centre 2e-integrals = 1.00 X 103-centre 2e-integrals = 1.00 X 10 12 12

• Schwarz screening = 5.95 X 10Schwarz screening = 5.95 X 10 10 10

• % 3c 2e-ints. In core = 100%% 3c 2e-ints. In core = 100%

Fock matrix (N = 3888)

0

20

40

60

80

100

120

16 32 64 128 256 512

PeIGS 2.1PeIGS 3.0PDSYEV (Scpk 1.5)PDSYEVD (Scpk 1.7)BFG-Jacobi (DL)

Number of processors

Tim

e (s

ec)



Multiple Time and Length ScalesMultiple Time and Length Scales

QM/MM - first step towards multiple length scalesQM/MM - first step towards multiple length scales QM treatment of the active siteQM treatment of the active site

reacting centrereacting centre problem structures (e.g. transition metal centres)problem structures (e.g. transition metal centres) excited state processes (e.g. spectroscopy)excited state processes (e.g. spectroscopy)

Classical MM treatment of environmentClassical MM treatment of environment enzyme structure, zeolite framework, explicitenzyme structure, zeolite framework, explicit

and/or dielectric solvent modelsand/or dielectric solvent models

Multiple time scale algorithms for MDMultiple time scale algorithms for MD Recompute different parts of energy expression at different intervals e.g. Recompute different parts of energy expression at different intervals e.g.

variants of the Reference System Propagation Algorithm (RESPA)variants of the Reference System Propagation Algorithm (RESPA)

But to date length / time scales only differ by ~ 1 order of magnitude For an example of an effort to link the atomistic and meso-scales see RealityGrid: http://www.realitygrid.org/information.html



• QM region 35 atoms (DFT BLYP) – include residues with possible proton donor/acceptor roles – GAMESS-UK, MNDO, TURBOMOLE

• MM region (4,180 atoms + 2 link)– CHARMM force-field, implemented in CHARMM, DL_POLY

Triosephosphate isomerase (TIM)

• Central reaction in glycolysis, catalytic interconversion ofDHAP to GAP

• Demonstration case within QUASI (Partners UZH, and BASF)

Triosephosphate isomerase (TIM)

• Central reaction in glycolysis, catalytic interconversion ofDHAP to GAP

• Demonstration case within QUASI (Partners UZH, and BASF)

QM/MM Applications

1467

1030

1487

714

802

540

778

419

508

308

428

274

196

257

213

0

400

800

1200

1600

8 16 32 64

CS7 AMD K7/1000 + SCI

CS9 P4/2000 + Myrinet 2k

SGI Origin3800/R14k-500

AlphaServer SC ES45/1000

Measured Time (seconds)

T T 128128 (O3800/R14k-500) = 181 secs (O3800/R14k-500) = 181 secs



From Teraflops to PetaflopsFrom Teraflops to Petaflops Short Term - Problem Scaling and Re-formulationShort Term - Problem Scaling and Re-formulation

Approaches to efficient exploitation of larger systemsApproaches to efficient exploitation of larger systemsOpportunities for more realistic modellingOpportunities for more realistic modellingNeed to avoid dependency on continued scaling of existing algorithmsNeed to avoid dependency on continued scaling of existing algorithms

Scientific Targets: Catalysis, Enzymes and Biomolecules, Heavy Particle Scientific Targets: Catalysis, Enzymes and Biomolecules, Heavy Particle DynamicsDynamics

Long Term - New HorizonsLong Term - New Horizons ““Simulation of whole systems, and not just system components”Simulation of whole systems, and not just system components” Automated problem solutionAutomated problem solution

Focus on parallel supercomputers build with commodity compute Focus on parallel supercomputers build with commodity compute servers tied by high performance communication fabricservers tied by high performance communication fabric

New PetaOPs architecture ProjectsNew PetaOPs architecture Projects IBM’s Blue LightIBM’s Blue Light

cellular architecture with 10cellular architecture with 1055 or more CPUs; intended to be general purpose or more CPUs; intended to be general purpose

IBM’s Blue GeneIBM’s Blue Gene collocation of 32 CPUs, 8MB RAM on same chip (may scale to 10collocation of 32 CPUs, 8MB RAM on same chip (may scale to 1066 CPUs) CPUs) application specific, protein foldingapplication specific, protein folding



“ “State of the Art”State of the Art”

Tera-flop computingTera-flop computing

~ 1000 Processors~ 1000 Processors

TimeTime

Same model but exploit faster executionSame model but exploit faster execution• Longer timescale for simulations• Interactive exploration! Limited scalability of current algorithms! Limited scalability of current algorithms! Long timescales demand higher accuracy! Long timescales demand higher accuracy

Size Size

More Accurate More Accurate MethodsMethods

• Better forcefields• Increased use of ab-initio methods• Higher level QM• QM/MM, DFT replacing semi-empirical methods• Finer numerical grids

!! Accuracy of Accuracy of methods tends to methods tends to increase slowly increase slowly with costwith cost !! Most major Most major challenges involve challenges involve larger systemslarger systems

Sampling Sampling

Study many configurations or systems at onceStudy many configurations or systems at once• Better Statistics, Free energies• Combinatorial methods• MC Ensembles!! Often satisfied by cheaper, commodity systemsOften satisfied by cheaper, commodity systems

Accuracy Accuracy

Larger problemsLarger problems

• Distributed data methods can exploit large global memories

!! Many algorithms Many algorithms contain serious contain serious bottlenecks, e.g. bottlenecks, e.g. diagonalisation diagonalisation O(NO(N33))

!! Sampling confor-Sampling confor-mational space mational space becomes harder becomes harder with system sizewith system size



What is Petaflop Computational What is Petaflop Computational ChemistryChemistry

Consider large classical simulationsConsider large classical simulations start from a typical 100,000 particle biomolecule + water start from a typical 100,000 particle biomolecule + water cost per timestep ~50 seconds on 1GF (peak) processorcost per timestep ~50 seconds on 1GF (peak) processor MD simulations of order 0.5MD simulations of order 0.5s will require approx 100,000,000 stepss will require approx 100,000,000 steps Cost 5,000 Pflops per state point i.e. 1.5 hours on a Pflop machineCost 5,000 Pflops per state point i.e. 1.5 hours on a Pflop machine Energy Scales as scales as O(N log (N)) and the equilibration time as Energy Scales as scales as O(N log (N)) and the equilibration time as

O(N O(N 5/35/3)) 1,000,000 particle simulation (for 25 us) will take 840 Pflop hour 1,000,000 particle simulation (for 25 us) will take 840 Pflop hour

(complex membrane protein)(complex membrane protein)

Quantum SimulationsQuantum Simulations Assuming a cost of 5 hours on 1 Gflop processor, 1 day on a 1 Pflop Assuming a cost of 5 hours on 1 Gflop processor, 1 day on a 1 Pflop

resource will simulate 25 nanoseconds of motion, corresponding to the resource will simulate 25 nanoseconds of motion, corresponding to the equilibration time of a 10,000 atom system.equilibration time of a 10,000 atom system.



Modelling of Catalysis Modelling of Catalysis 1. Current Status1. Current Status

ToolsTools Classical simulationClassical simulation

equilibrium structures and transport propertiesequilibrium structures and transport properties QM simulationsQM simulations

reactivity of molecular models, surface structures, supercellsreactivity of molecular models, surface structures, supercells QM/MM methodsQM/MM methods

solvent, ligand and lattice effects on local chemistrysolvent, ligand and lattice effects on local chemistry

Scientific DriversScientific Drivers From mechanisms to reaction ratesFrom mechanisms to reaction rates From simplified models to multi-component systems, defect sitesFrom simplified models to multi-component systems, defect sites

Collaboration QUASI (Quantum Simulation in Industry) Collaboration QUASI (Quantum Simulation in Industry) see http://www.cse.clrc.ac.uk/qcg/quasisee http://www.cse.clrc.ac.uk/qcg/quasi



Modelling of CatalysisModelling of Catalysis 2. Towards Petaflops2. Towards Petaflops

Extended dynamical simulation, solvent, counterions etc Extended dynamical simulation, solvent, counterions etc

Advanced forcefields (polarisabilities, cross-terms etc)Advanced forcefields (polarisabilities, cross-terms etc) More extensive use of quantum methods (large DFT More extensive use of quantum methods (large DFT

clusters and periodic supercells)clusters and periodic supercells)

Combinatorial approach to catalyst formulationCombinatorial approach to catalyst formulation Quantum dynamics & tunnelling Quantum dynamics & tunnelling viavia path integral methods path integral methods Finite difference approach to local surface vibrationsFinite difference approach to local surface vibrations

ab-initioab-initio treatment of larger surface domains treatment of larger surface domains

TimeTime

SizeSize

Sampling Sampling

AccuracyAccuracy



Require integrated models spanning time and length scales.Require integrated models spanning time and length scales. Initially the interface between the length and time scales will be via Initially the interface between the length and time scales will be via

construction of parametric modelsconstruction of parametric models There is also the possibility that lower-level data might be computed on There is also the possibility that lower-level data might be computed on

demand as required to sustain the accuracy of the large-scale simulationdemand as required to sustain the accuracy of the large-scale simulation

Reactor geometriesReactor geometries

Diffusion of reactants and productsDiffusion of reactants and products

Heterogeneous surface structures (films etc)Heterogeneous surface structures (films etc)

Reaction ratesReaction rates

Detailed chemical energeticsDetailed chemical energetics

Modelling of CatalysisModelling of Catalysis3. New Horizons3. New Horizons



Biomolecular Simulation Biomolecular Simulation 1. Current Status 1. Current Status

ToolsTools Classical SimulationClassical Simulation

Simple but well established forcefields for proteins, nucleic acids, Simple but well established forcefields for proteins, nucleic acids, polysacharrides etc polysacharrides etc

QM and QM/MMQM and QM/MMenzyme reaction energetics, ligand bindingenzyme reaction energetics, ligand binding

Continuum electrostaticsContinuum electrostaticsPoisson-Boltzmann, Generalised BornPoisson-Boltzmann, Generalised Born

Statistical MechanicsStatistical Mechanics Scientific TargetsScientific Targets

Accurate free energies for more complex systemsAccurate free energies for more complex systems Faster and more accurate screening of protein / ligand binding Faster and more accurate screening of protein / ligand binding Membrane proteins (e.g. receptors)Membrane proteins (e.g. receptors) Complex conformational changes (e.g. protein folding)Complex conformational changes (e.g. protein folding) Excited state dynamicsExcited state dynamics



Biomolecular SimulationBiomolecular Simulation2. Towards Petaflops2. Towards Petaflops

Faster simulations to approach real timescales Faster simulations to approach real timescales !! major scalability problems!! major scalability problems

New forcefields incorporating polarisation, cross-terms, etcNew forcefields incorporating polarisation, cross-terms, etc Increased use of Increased use of ab-initioab-initio methods methods

Tremendous potential due to importance of free energies.Tremendous potential due to importance of free energies. Multiple independent simulationsMultiple independent simulations Replica path - simultaneous minimisation or simulations of an Replica path - simultaneous minimisation or simulations of an

entire reaction pathwayentire reaction pathway Replica exchange - Monte Carlo exchange of configurations Replica exchange - Monte Carlo exchange of configurations

between an ensemble of replicas at different temperaturesbetween an ensemble of replicas at different temperatures Combinatorial approach to ligand bindingCombinatorial approach to ligand binding

membranes, molecular assemblies … membranes, molecular assemblies …

TimeTime

SizeSize

Sampling Sampling

AccuracyAccuracy



Replica Path MethodsReplica Path Methods

Replica path method - simultaneously optimise a series of Replica path method - simultaneously optimise a series of points defining a reaction path or conformational change, points defining a reaction path or conformational change, subject to path constraints.subject to path constraints.

Suitable for QM and QM/MM HamiltoniansSuitable for QM and QM/MM Hamiltonians

Parallelisation per point Parallelisation per point

Communication is limited to Communication is limited to adjacent points on the path - adjacent points on the path - global sum of energy functionglobal sum of energy function

PP3636

PP44

PP3232 PP3333

PP11PP00

PP3434 PP3535

PP33PP22

EE

Reaction Co-ordinateReaction Co-ordinate

Collaboration with Bernie Brooks (NIH) Collaboration with Bernie Brooks (NIH) http://www.cse.clrc.ac.uk/qcg/chmgukhttp://www.cse.clrc.ac.uk/qcg/chmguk



Biomolecular Simulation Biomolecular Simulation 3. New Horizons3. New Horizons

Towards full quantum simulation (e.g. Car Parrinello)Towards full quantum simulation (e.g. Car Parrinello)

Towards Whole cell simulationTowards Whole cell simulation Mechanical deformation, Electrical behaviourMechanical deformation, Electrical behaviour

Diffusion of polymeric molecules (e.g. neuro-transmitters) by DPDDiffusion of polymeric molecules (e.g. neuro-transmitters) by DPD

Nanoscale models for supra-molecular Nanoscale models for supra-molecular structures (e.g. actin filaments in muscles)structures (e.g. actin filaments in muscles)

Atomistic Molecular DynamicsAtomistic Molecular Dynamics

Quantum chemistry of reacting sites Quantum chemistry of reacting sites

CCP1 Flagship project - Simulation of Condensed Phase CCP1 Flagship project - Simulation of Condensed Phase Reativity: http://www.ccp1.ac.uk/projects.shtmlReativity: http://www.ccp1.ac.uk/projects.shtml



Heavy Particle Dynamics Heavy Particle Dynamics 1. Current Status1. Current Status

ToolsTools Many methods require evaluation of energies on a massive multi-Many methods require evaluation of energies on a massive multi-

dimensional griddimensional gridUse high-level computational chemistry methods (e.g. NWChem, Use high-level computational chemistry methods (e.g. NWChem,

MOLPRO), together with task farming.MOLPRO), together with task farming. Complex parameter fittingComplex parameter fitting

Can exploit interactivity (INOLLS) incorporating experimental dataCan exploit interactivity (INOLLS) incorporating experimental data Dynamical Simulation MethodsDynamical Simulation Methods

Wavepacket evolution on a GridWavepacket evolution on a GridClassical path methods (multiple direct dynamics trajectories)Classical path methods (multiple direct dynamics trajectories)Variational solutions for spectroscopic levelsVariational solutions for spectroscopic levels

TargetsTargets Larger species (5 atoms and beyond)Larger species (5 atoms and beyond) Influence of surrounding molecules (master equation)Influence of surrounding molecules (master equation)

CCP6 Collaboration, ChemReact consortium on national HPC facilities CCP6 Collaboration, ChemReact consortium on national HPC facilities



Heavy Particle DynamicsHeavy Particle Dynamics2. Towards Petaflops2. Towards Petaflops

Interactivity in potential energy surface fitting.Interactivity in potential energy surface fitting. Longer time simulations (slower reactions)Longer time simulations (slower reactions)

PE surfaces from large basis set CCSD(T) etcPE surfaces from large basis set CCSD(T) etc MRCI for excited states and couplingsMRCI for excited states and couplings

Large grids for higher dimensionality systems (5 atoms)Large grids for higher dimensionality systems (5 atoms) Multiple coupled PE surfaces (involvement of excited Multiple coupled PE surfaces (involvement of excited

states) states) J > 0 - additional angular momentum statesJ > 0 - additional angular momentum states

Larger grids for wavepacket evolution Larger grids for wavepacket evolution

TimeTime

SizeSize

Sampling Sampling

AccuracyAccuracy



Integration with Combustion, detonation, atmospheric models, most Integration with Combustion, detonation, atmospheric models, most likely through detailed parameter and rate constant derivationlikely through detailed parameter and rate constant derivation

CFD simulation (transient or steady state, CFD simulation (transient or steady state,

turbulence models etc)turbulence models etc)

Reduced chemical kineticsReduced chemical kinetics

Reaction rates, sensitivity to pressure and temperatureReaction rates, sensitivity to pressure and temperature

Heavy particle dynamicsHeavy particle dynamics

Heavy Particle DynamicsHeavy Particle Dynamics3. New Horizons3. New Horizons



The Software ChallengeThe Software Challenge Lack of sustained focus within the Chemical Sciences for responding Lack of sustained focus within the Chemical Sciences for responding

to the challenges of Petascale Computingto the challenges of Petascale Computing The impact of providing such a focus periodically demonstrated at The impact of providing such a focus periodically demonstrated at

points on the road to Terascale Computingpoints on the road to Terascale Computing The HPCC Grand Challenge projectsThe HPCC Grand Challenge projects NWChem - DOE (PNNL) - principally Electronic Structure;NWChem - DOE (PNNL) - principally Electronic Structure; NAMD - NIH funded initiative in bio-molecular sciences and Classical MD.NAMD - NIH funded initiative in bio-molecular sciences and Classical MD.

Such Initiatives demand: Such Initiatives demand: The successful integration of multi-disciplinary teams including Application The successful integration of multi-disciplinary teams including Application

and Computational scientists, Computer Scientists and Mathematicians;and Computational scientists, Computer Scientists and Mathematicians; A long term commitment to the challenge, with funding in place to respond A long term commitment to the challenge, with funding in place to respond

to the inevitable pace of architecture / hardware change.to the inevitable pace of architecture / hardware change. Relying on the efforts of individual groups to overcome this software Relying on the efforts of individual groups to overcome this software

challenge challenge will not workwill not work..



Problem Solving EnvironmentsProblem Solving EnvironmentsRequirement:Requirement: A comprehensive A comprehensive problem solving environment (PSE) for problem solving environment (PSE) for molecular modeling and simulation. molecular modeling and simulation. Key components include:Key components include:

• common graphical user interfacescommon graphical user interfaces• scientific modelling managementscientific modelling management• seamless transfer of information seamless transfer of information

between applicationsbetween applications• persistent data storagepersistent data storage• integrated scientific data managementintegrated scientific data management• tools for ensuring efficient use of tools for ensuring efficient use of

computing resources across a computing resources across a distributed network i.e. GRIDdistributed network i.e. GRID

• visualization of multi-dimensional data visualization of multi-dimensional data structuresstructures



Summary and Recommendations 1.Summary and Recommendations 1.

Computational chemistry on Tera-scale resources “needs work”, but Computational chemistry on Tera-scale resources “needs work”, but there are plenty of opportunities to advance there are plenty of opportunities to advance collaborativelycollaboratively chemical chemical sciences “towards petaflops”sciences “towards petaflops”

The short term priority is scaling and adapting current methodologiesThe short term priority is scaling and adapting current methodologies Advancing use of distributed data algorithmsAdvancing use of distributed data algorithms O(N) techniques to remove bottlenecks and enhance scalabilityO(N) techniques to remove bottlenecks and enhance scalability Detailed work on parallel scaling Detailed work on parallel scaling

– Library developmentsLibrary developments

– Performance analysis and prediction toolsPerformance analysis and prediction tools Re-formulation of problems in terms of more weakly interacting Re-formulation of problems in terms of more weakly interacting

ensemblesensembles Parallel implementations of more complex physical modelsParallel implementations of more complex physical models Automation, data handling, PSEs for combinatorial workAutomation, data handling, PSEs for combinatorial work

In the longer term by tackling more integrated problemsIn the longer term by tackling more integrated problems Modularity of softwareModularity of software Science of the time / length scale interfacesScience of the time / length scale interfaces



Summary and Recommendations 2.Summary and Recommendations 2. Investment in Software: “Code Sharing”Investment in Software: “Code Sharing”

UK has kept a strong applications focus, but has lagged behind the US in UK has kept a strong applications focus, but has lagged behind the US in the radical re-design of simulation packagesthe radical re-design of simulation packages

Sustained investment in Petascale Software DevelopmentSustained investment in Petascale Software Development Current UK and International collaborationsCurrent UK and International collaborations

Scalable QC algorithmsScalable QC algorithmsNWChem, MOLPRO, GAMESS-UK (PNNL, ORNL, SDSC, DL, NWChem, MOLPRO, GAMESS-UK (PNNL, ORNL, SDSC, DL,

CCP1/5/6); CCP1/5/6); Replica path methods in CHARMM/GAMESS-UKReplica path methods in CHARMM/GAMESS-UK

DL collaboration with NIH, PSC; DL collaboration with NIH, PSC; Flexible QM/MM models incorporating classical polarisation Flexible QM/MM models incorporating classical polarisation

ChemShell / GULP / GAMESS-UK / ChemShell / GULP / GAMESS-UK / NAMDNAMD;; Distributed data classical models, electrostatic models Distributed data classical models, electrostatic models

DL_POLY, NWChem – CCP1/5.DL_POLY, NWChem – CCP1/5. Stronger Links between UK initiatives and US programs SciDAC and Stronger Links between UK initiatives and US programs SciDAC and

NSF PACI, NPACI etc.NSF PACI, NPACI etc.

beyond teraflops toward petaflops 2 october 2002 computational science and engineering department...

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