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A Workflow Management & Grid Computing Approach to Molecular Simulation-based Experiments

A Workflow Management & Grid Computing Approach to Molecular Simulation-based Experiments. Karpjoo Jeong ( jeongk@konkuk.ac.kr ), Konkuk Univ., Seoul, Korea. Suntae Hwang ( sthwang@kookmin.ac.kr ), Kookmin Univ., Seoul, Korea. Collaborators. Bio/Nano Technology Seunho Jung, Konkuk Univ.

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A Workflow Management & Grid Computing Approach to Molecular Simulation-based Experiments

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  1. A Workflow Management & Grid Computing Approach to Molecular Simulation-based Experiments KarpjooJeong (jeongk@konkuk.ac.kr), Konkuk Univ., Seoul, Korea. Suntae Hwang (sthwang@kookmin.ac.kr), Kookmin Univ., Seoul, Korea.

  2. Collaborators • Bio/Nano Technology • Seunho Jung, Konkuk Univ. • Yoongho Im, Konkuk Univ. • Information Technology • Karpjoo Jeong, Konkuk Univ. • Suntae Hwang, Kookmin Univ. • Computational Science • Jysoo Lee, KISTI • June-Yub Lee, Ewha Univ.

  3. Convenient Experimental Environment Collaboration BT/NT: Analyzing Bio/Nano Materials Cheap Computing Power System Verification Requirements B/N/IT: Developing GRID-based Molecular Simulation Environment IT: Developing PC Lab-based Virtual Parallel Computers

  4. Goals • Develop simulation platform on computational grids • Provide scientists with common simulation platforms that they can share • Improve the utilization of computing resources by sharing • Improve research productivity • Allow scientists to focus on problem analysis • Enable scientists to speed up experiments • More concurrent experiments (examine more alternatives) • Early termination of meaningless simulation

  5. Our Approach • Simple Simulation Platform on Computational Grids • Provide a large scale distributed computing platform for molecular simulation which can be shared by a number of scientists • Workflow Management System • Support centralized monitor and control • Workflow system helps scientists to monitor and control research tasks in a single user-friendly GUI • Hide complicated distributed, heterogeneous platform issues from scientists • Allow scientists to deal with multiple simulation experiments

  6. KISTI Supercomputer CHARMM Task CHARMM Task CHARMM Task Konkuk Univ Kookmin Univ Big CHARMM System Structure Agent I/O Files Computational Grids User Workstation (GUI) workflow Workflow Engine Agent Small CHARMM I/O Files

  7. Workflow SystemPrototype Implementation (underway)

  8. Simulation Platform on Computational Grids • Compute servers • Own simulation packages • Run simulation jobs independently • Grid-based middleware • Support remote execution, file transfer, and lookup service • Agent dedicated to each legacy simulation package • Provide standard interface to legacy software packages • Can handle I/O files and program invocation • Support remote monitoring and control

  9. Simulation PlatformPrototype Implementation (underway) • Remote Invocation and File Transfer • Implemented by the GLOBUS toolkit • GRAM for remote invocation • GridFTP for file transfer • CA for authentication • Fault-tolerant Compute Servers • Implemented by Persistent Linda (fault-tolerant shared memory system) • Agents • Implemented as Persistent Linda application

  10. Remote Site Prototype Settings Gateway agent • Simple client on remote host • Persistent Linda System • CHARM driver programs on 21 Linux servers (P4 2.0Ghz) which invoke CHARM GridFTP CHARMM Driver Persistent Linda CHARMM Driver Linux Server GRAM CHARMM agent Linux Server CHARMM Driver CHARMM charmm job charmm result Linux Server

  11. Technical Issues being addressed • Real time monitoring and human interventions for remote simulation execution • To figure out as early as possible if current simulation job is worth continuing • Terminate execution if necessary • Legacy software package support • Knowledge about I/O files. Names, content formats and locations • Knowledge about invocation methods

  12. Wide-area distributed file systems with global synchronization mechanisms • To handle I/O files for simulation jobs distributed on the computational grids • Scheduling and Resource Management Techniques for Long-running Jobs • A single execution may take a few hours to a few months • Either dedicate a machine to each simulation job or migrate simulation job during execution • Predicting execution time may be possible

  13. Current Status and Future Plan • Prototype implementation is underway; Optimistically, the prototype will be almost fully functional by the middle of this year • Then, will be applied for real simulation experiments • At the same time, we are, however, also evaluating our design and changing it substantially; this may delay our plan • Design alternatives • New grid middleware tools • We are also planning to extend our system to be designed as a toolkit

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