“grid platform for drug discovery” project

2003/10/3UK Jpana N+N Meeting

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“Grid Platform for Drug Discovery” Project

Mitsuhisa Sato

Center for Computational Physics,University of Tsukuba, Japan


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Our Grid Project• JST-ACT program: “Grid platform for drug discovery”, fund

ed by JST(Japan Science and Technology Corporation), 1.3 M$/ 3 years started from 2001– Tokushima University, Toyohashi Inst. Of Tech., University of Tsuk

uba, Fuji Res. Inst. Corp.

• ILDG: International Lattice QCD Data Grid– CCP, U. of Tsukuba, EPCC UK, SciDAC US.– Design of QCDML– QCD Meta database by web services, QCD data sharing by SRM a

nd Globus replica …


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High Throughput Computing for drug discovery

• Exhaustive parallel conformation search and docking over Grid

• Accumulation computing results into large scale database and reuse

• High performance ab initio MO calculation for large molecules on clusters

“Combinatorial Computing”Using Grid


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Grid applications of our drug-discovery• Conformation search : find possible confirmations• Docking search: compute energy of combination of molecules • Quantitative Structure-Activity Relationships (SQAR) analysis:

finding rules of drug design

ConformationSearch

Druglibraries

Docking Search

(using ab initio MO calculation)

Conform-ations

DockingComputation

results

QSARanalysis

target

CONFLEX-GGrid enabled Conformation

Search application

MO in clustersJob submission for MO

Coarse-grain MO for Grid(REMD, FMO)

Design of XML for results

Web service interface


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Stepwise Rotation

Corner Flap

Edge Flip

CONFLEX• Algorithm: tree searchAlgorithm: tree search

– Local conformation changesLocal conformation changes

– Initial conformation selectionInitial conformation selection

• We are implementing with We are implementing with

OmniRPCOmniRPC

– Tree search action is dynamic!!!Tree search action is dynamic!!! Conformation search tree

AntiE=0.0 kcal/mol

Gauche +E=0.9 kcal/mol

Gauche -

E=0.9 kcal/mol


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Gird Platform for drug discovery

Univ. of Tsukuba AIST

Toyohashi Inst. Of Tech.Tokushima Univ.

Control & monitoring•Scheduling and monitoring of computations•distributed data base management•Design of Ｇ rid middleware

Development of large-scaleab-initio MO program

Cluster for CONFLEXdevelopment of conformation searchprogram (CONFLEX)

Database for CONFLEX results

Database of MO calculation results

3D structure databasefor drug design

wide-area networkwide-area network

request

requestrequest

request


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What can Grid do?Parallel Applications, programming, and our view　 for grid

• “Typical” Grid Applications– Parametric execution: Execute the

same program with different parameters using an large amount of computing resources

– master-workers type of parallel program

• “Typical” Grid Resources– A Cluster of Clusters: some PC

Clusters are available– Dynamic resources: load and status are changed time-to-time.

ＰＣＰＣＰＣＰＣ

PC ClusterPC Cluster



Our View



Grid Grid EnvironmeEnvironme

ntnt

Grid Grid EnvironmeEnvironme

ntnt


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Parallel programming in Grid– Using Globus shell (GSH)

• Submit batch job scripts to remote nodes• staging and workflow

– Grid MPI (MPICH-G, PACX MPI, …)• General-purpose, but difficult and error-prone • No support for dynamic resource and fault-tolerance• No support for Firewall, clusters with private network.

– Grid RPC• a good and intuitive programming interface• Ninf, NetSolve, …

OmniRPCOmniRPC


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Overview of OmniRPCA Grid RPC system for parallel computing

• Provide seamless parallel programming environment from clusters to grid. – It use “rsh” for a cluster, “GRAM” for a grid managed by Globus, “ssh” for a conve

ntional remote nodes.– Program development and testing in PC clusters– Product run in Grid to exploit huge computing resources– User can switch configuration with “host file” without any modification

• Make use of remote clusters of PC/SMP as Grid computing resource– Support for clusters in firewall and private address

PCPC


PCPCPCPC

PCPC

PCPC


PCPCPCPC

PCPC

PCPC


PCPCPCPC

PCPC

PCPC


PCPCPCPC

PCPC

ClientClient

Grid Grid EnvironmentEnvironment<? xml version=“1.0 ?>

<OmniRpcConfig> <Host name=“dennis.omni.hpcc.jp” > <Agent invoker=“globus” mxio=“on”/> <JobScheduler type=“rr” maxjob=“20”/></Host></OmniRpcConfig>

Host fileHost file


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Overview of OmniRPC (cont.)

• Easy-to-use parallel programming interface

– A gridRPC based on Ninf Grid RPC– Parallel programming using asynchronou

s call API– The thread-safe RPC design allows to us

e OpenMP in client programs

• Support Master-workers parallel programs for parametric search grid applications

– Persistent data support in remote workers for applications which requires large data

• Monitor and performance tools

int main(int argc, char **argv){ int i, A[100][100],B[100][100][100],C[100][100][100];

OmniRpcRequest reqs[100]; OmniRpcInit(&argc, &argv);

for(i = 0; i< 100; i++) reqs[i] = OmniRpcCallAsync(“mul”,100, B[i], A, C[i]); OmniRpcWaitAll(100,reqs); . OmniRpcFinalize(); return 0;}


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OmniRPC features• need Globus?

– No, you can use “ssh” as well as “globus”– It is very useful for an application people.– “ssh” can solve “firewall” problem.

• Data persistence model?– Parameter search type application need to share the initial data. – OmniRPC support it.

• Can use many (remote) clusters?– Yes, OmniRPC supports “cluster of clusters”.

• How to use in different machine and environment ?– You can switch the configuration by “config file” without modification on so

urce program.

• Why not “Grid PRC” standard?– OmniRPC provides high level interface, to avoid “scheduling” and “fault-tol

erance” from users.


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OmniRPC Home Page

http://www.omni.hpcc.jp/omnirpc/





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Conflex from Cluster to Grid

• For large bimolecules, the number of combinational trial structure will be huge!

• Geometry optimization of large molecular structures requires more time to compute!

• Geometry optimization phase takes more than 90% in total execution time

• So far, executed on PC Cluster by using MPI

Grid allows to use huge computing resources to overcome these problem!


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Our Grid Platform

Univ. of TsukubaUniv. of TsukubaDennis ClusterDennis ClusterDual P4 Xeon 2.4GHzDual P4 Xeon 2.4GHz10 nodes10 nodesAlice ClusterAlice ClusterDual Athlon 1800+Dual Athlon 1800+14 nodes14 nodes

AISTAISTUME ClusterUME ClusterDual P3 1.4GHzDual P3 1.4GHz32 nodes32 nodes

Tokushima Univ.Tokushima Univ.Toku ClusterToku ClusterP3 1.0GHzP3 1.0GHz8 nodes8 nodes

Toyohashi Univ. of Toyohashi Univ. of Tech.Tech.Toyo ClusterToyo ClusterDual Athlon 2000+Dual Athlon 2000+8 nodes8 nodes

Tsukuba Tsukuba WANWAN

SINETSINET


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Summary of Our Grid Environment

Dual P3 1.4GHz

P3 1GHz

Dual Athlon 1800+

Dual Athlon 1800+

Dual P4 Xeon 2.4GHz

Machine overview

32

88

1410

# of Nodes

--Dennis

0.6924.40Toku

2.122.73UME

0.5513.00Toyo

11.220.18Alice

Throughput (MB/s)#

RTT ＊（ ms)#

Cluster

＊ Round-Trip Time# All measurement Dennis Cluster and Each Cluster


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CONFLEX-G:Grid enabled CONFLEX

• Parallelize molecular geometry optimization phase using Master/Worker model.

• OmniRPC persistent data model (automatic initializable remote module facility) allows to reuse workers for each call.

– Eliminate initializing worker program at every PRC.

Selection of Initial Structure

LocalPerturbation

GeometryOptimization

Comparison & Store

ConformationDatabase

PC

PC Cluster A

PCPCPC

PC

PC Cluster B

PCPCPCPC

PC Cluster C

PCPCPC


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Experiment Setting• CONFLEX’s version: 402q• Test data: Two Molecular samples

– C17 (51 atoms)

– AlaX16a (181 atoms).

• Authentication method :SSH • CONFLEX-G client program was executed on the server node of

Dennis cluster• We used all nodes in clusters of our grid


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Sample Molecules

320

522

# of opt. trial structures

96000= 26.7(h)

300160AlaX16a(181 atoms)

8351.6 48C17(51 atoms)

Estimated total exec.time for all Trial structures in Dennis’s Single CPU (s)

Average exec. time to opt. trial structure (s)

# of trial structure at one opt. phase(degree of parallelism)

data


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Comparison between OmniRPC and MPI in Dennis Cluster

C17 （ 51 atoms, degree of parallelism 48 ）

0

200

400

600

800

1000

1200

1 2 4 8 16 20

Number of Workers

Tot

al e

xecu

tion

Tim

e (s

)

SequentialMPIOmniRPC

Overhead of 　 On-Demand Initialization of worker program in OmniRPC

10 times Speedup using OmniRPC


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Execution time of AlaX16a(181 atoms, degree of parallelism 160)

0 500 100015002000250030003500

Total execution time (s)

Dennis MPI(20w)Dennis(20w)

Alice(28w)

UME(64w)Dennis+Alice(48w)

Dennis+UME(84w)Alice+UME(92w)

Dennis+Alice+UME(112w) 64 times Speedup


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Discussion

• Performance of CONFLEX-G was observed to be almost equals to that of CONFLEX with MPI– Overheads to initialize workers was found. It will be required to imporve.

• We could achieve performance improvement using multiple clusters, – A speedup of 64 on 112 workers in AlaX16a(181 atoms)– However … , In our experiment:

• Each workers takes only one or two trial structures, too few!• Load in-balance occurs because exec. time of each opt. varies.

• We expect more speed up for larger molecule.


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Discussion (cont’d)

• Possible improvement:– Exploit more parallelism

• Parallelize the outer loop to increase the number of structure optimization at a time

– Efficient Job Scheduling • Heavy jobs -> fast machines• light jobs -> slow machines

– Can we estimate execution time ?

– Parallelize worker program by SMP(OpenMP)• Increase the performance of worker• Reduce the number of workers


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Summary and Future work• Conflex-G: Grid-enabled molecular confirmation search.

– We used OmniRPC to make it grid-enabled.– We are actually doing product-run..

• For MO simulation (Docking), we are working on coarse-grain MO, as well as job submission– REMD (replica exchange program using NAMD)– FMO (Fragment MO)

• For QSAR– Design of ML to describe computation results– Web service interface to access the database

“grid platform for drug discovery” project

Documents

grid platform

drug discoveryuniv

grid rpca good

parallel applications

large molecules

university of tsukuba

pc clusters

program deve