Supporting Molecular Simulation-based Bio/Nano Research on Computational GRIDs Karpjoo Jeong ([email protected]), Konkuk [email protected] Suntae

Supporting Molecular Simulation-based Bio/Nano Research

on Computational GRIDs

Karpjoo Jeong ([email protected]), Konkuk Univ.

Suntae Hwang ([email protected]), Kookmin Univ.

mailto:[email protected]



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CollaborationCollaboration

IT– Karpjoo Jeong at Konkuk University– Suntae Hwang at Kookmin University– Younghwan Park at Hansung University

BT/NT– Seunho Jung at Konkuk University– Yoongho Im at Konkuk University

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ContentsContents

Molecular Simulation-based BioNano Research

How to Build Cheap but Powerful Supercomputer

How to Manage Lots of Simulation Results from Supercomputers

Molecular Simulation System on World-wide Computational GRIDs

Implementation Status and Preliminary Performance Results

Conclusions and Future Work

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Molecular Simulation-based Molecular Simulation-based BioBioNano ResearchNano Research

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CharacteristicsCharacteristics

Requirements for very large computation Complicated research process in a

workflow style– Consist of Modeling, Simulation, Verification

Tasks which form a complex workflow Credibility of simulation tool is crucial

– A few well-known software packages are only accepted

Lots of repetition of same simulation and application to similar problems– But with different parameters

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Characteristics (Cont’d)Characteristics (Cont’d)

Good News– Lots of parallelism in research tasks– No need for writing complicated simulation

code (in most cases) Bad News

– Frequent scientists’ intervention is required Verify intermediate results Guide simulation directions

– Single task (single instance of simulation execution) alone may be very large

– Parallel simulation is extremely difficult

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Potentials for GRIDPotentials for GRID

Inter-Task Independence and Parallelism Problem-level Parallelism

– Most coarse-grained– Solving similar problems by similar methods

Simulation-level Parallelism– Coarse-grained– Repetition of same simulation but with

different parameters

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How to Build Cheap and How to Build Cheap and Powerful SupercomputerPowerful Supercomputer

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PC Lab-based Virtual Parallel Computers

Goals– Utilize idle computing resources at many PC

labs, universities around the world Hundreds or thousands of PCs at each university

which are almost 100% idle at night Relatively less sensitive to security issues

– Build these PCs into virtual parallel computers a thousand of Pentium4 2.0GHz CPUs can match

very expensive supercomputers

– Apply these parallel computers for coarse-grained parallel problems such as molecular simulation-based bio/nano research problems

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Vision: World-wide Computing

Night-time workDay-time Work

Migration

Project1

Prject2

MolecularSimulationMolecular

SimulationMolecularSimulationMolecular

Simulation



Simulation

Project1

Prject2



Simulation



Simulation

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Base Computing SystemBase Computing System

Persistent Linda Parallel/Distributed System Linda Parallel Programming Model

– Shared Memory Model in Mailbox Style– Ease of programming

Heterogeneity Support (Ex, Linux 및 MS Windows)– Process Migration– Parallel Computation Migration

Fault Tolerance IDLE PC Utilization Efficient Support for Coarse-grained Parallel

Computation

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PrototypePrototype

PC Lab at College of Information and Communication, Konkuk University, Seoul, Korea

50 Pentium4 PCs Linux cluster with 5 nodes

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Web Monitoring InterfaceWeb Monitoring Interface

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How to Manage Lots of How to Manage Lots of Simulation Results from Simulation Results from

SupercomputersSupercomputers

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Workflow ApproachWorkflow Approach

Provide workflow-based simulation environment– Allow scientists to plan research processes

in a workflow style – Manage intermediate results and notify

scientists of next tasks– Execute independent tasks in parallel

Scientists can avoid tedious management overheads and focus on planning, analysis and verification work

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GRID-based Molecular GRID-based Molecular Simulation EnvironmentSimulation Environment

Workflow-based simulation environment submits simulation tasks to computational GRIDs in a user-transparent way

Computational GRIDs

• Shared computing resources• CHARMM, AMBER Tasks• Numerous independent tasks

Computational GRIDs


Workflow-basedSimulation Environment

Project1MolecularSimulationMolecularSimulationMolecular

SimulationMolecularSimulationPrject2

MolecularSimulationMolecularSimulationMolecularSimulationMolecularSimulation

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Computational GRIDs


Computational GRIDs

• Shared computing resources• CHARMM, AMBER Tasks• Numerous independent tasksWorkflow-based

Simulation Environment

Project1MolecularSimulationMolecular


SimulationPrject2MolecularSimulationMolecular


Simulation Workflow-basedSimulation Environment





Simulation






Simulation

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Molecular Simulation System onMolecular Simulation System onWorld-wide Computation GRIDsWorld-wide Computation GRIDs((Persistent Linda and Globus)Persistent Linda and Globus)

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Persistent Linda • Shared computing resources• CHARMM, AMBER Tasks• Numerous independent tasks

Persistent Linda • Shared computing resources• CHARMM, AMBER Tasks• Numerous independent tasks

Gateway Agent

GRAM

GridFTP

Ex. Konkuk Univ.

Ex. KISTI

Data/ResultFiles

TaskRequest

MolecularSimulation

GLOBUSWorkflow-based

Simulation Environment





Simulation

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Fault Tolerance and MigrationFault Tolerance and Migration

Simulation packages such as CHARM and GAUSSIAN support checkpointing facilities– Save computation status to disk and resume

computation from it later Our Molecular Simulation System is

designed to use these facilities to deal with fault tolerance and migration

Checkpointing is a solution to long-running simulation

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PersistentLinda

PersistentLinda






Simulation






Simulation

PersistentLinda

PersistentLinda

GLOBUSGLOBUS






Simulation

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Implementation Status and Implementation Status and Preliminary Performance ResultsPreliminary Performance Results

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Implementation StatusImplementation Status

Persistent Linda System– Used for various parallel applications for

years– Recently ported to MS Windows

Workflow Molecular Simulation System– Implementation of prototype is underway

Globus-based global coordination middleware– Gateway between Persistent Linda and

Globus is implemented– Global scheduler is being designed

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ExperimentsExperiments Settings

– Simple client on remote host– Persistent Linda System– Three CHARM driver programs on three Linux

servers (P4 2.0Ghz) which invoke CHARM Scenario: remote invocation of single CHARM task Result

– about 30 seconds for remote invocation overhead

Persistent Linda

Persistent Linda

CHARMMDriver

Linux ServerCHARMMDriver

Linux ServerCHARMM

Driver

Linux Server

GRAM

GridFTP

Gatewayagent

CHARMMCHARMM

agentcharmm job

charmm result

Remote Site

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Conclusions and Future WorkConclusions and Future Work

Propose molecular simulation system on computational GRIDs– Utilize idle PCs at university Labs– Workflow-based simulation environment

Effective for coarse-grained parallel problems such as molecular simulation-based bio/nano research

Developing Globus-based global middleware Planning on

– Large scale computational GRIDs by combining several university labs

– Application for bio/nano research Database for chiral molecules

Documents

Supporting Molecular Simulation-based Bio/Nano Research on Computational GRIDs Karpjoo Jeong ([email protected]), Konkuk [email protected] Suntae