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The Design of a Grid Computing System for Drug Discovery and Design

The Design of a Grid Computing System for Drug Discovery and Design. David Gobaud Computational Drug Discovery Stanford University 14 March 2006. Outline. Overview Network Topology Structure Summary Resource Management Agent Job Submission Client Scheduler Node Execution Node

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The Design of a Grid Computing System for Drug Discovery and Design

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  1. The Design of a Grid Computing System for Drug Discovery and Design David Gobaud Computational Drug Discovery Stanford University 14 March 2006

  2. Outline • Overview • Network Topology • Structure Summary • Resource Management Agent • Job Submission Client • Scheduler Node • Execution Node • Experimental Results • Questions

  3. Overview • Novel Grid computing system for drug discovery and design (DDG) • Use idle resources donated by nodes over the internet • P2P technology • Hybrid resource management architecture • Avoid master/slave problems

  4. Network Topology • Centralized + Decentralized P2P • Clusters that wish to compute join DDG and become execution nodes • Grouped into Virtual Organizations (VO) by structures and interests • Communicate via Resource Management Agents (RMA) in P2P mode • One peer fails – other peers can compensate • Each local RMA captures and maintains resource info of a VO

  5. Structure Summary • End-users submit protein molecule files to DDG via web portal • End-users create job request template for a new application • Specify QoS requirements • Job submitted to Scheduler Node by Submission Node • DDG provides a distributed repository where execution nodes publish their real-time workload info

  6. Structure Summary • Scheduler Node • Divide job into many parallel sub-jobs • Query repository to find and allocate execution nodes for sub-jobs to satisfy QoS requirements • Combine results from sub-jobs into whole docking result • Return result to submission node which informs the user the job is complete

  7. High-Level Workflow of DDG

  8. Experimental Results • Feasibility and performance evaluated by protein molecules docking experiments • Many DBs used – Specs, ACD, CNPP, and NCBI • 20 PC LAN • 1Ghz Pentium IVs • 256 Mb RAM • 40 Gb hard drives • 100 Mb/sec Ethernet • SGI Origin 3800 cluster of 64 processors • SUNWAY cluster of 32 processors • SUNWAY cluster of 64 processors • SUNWAY cluster of 256 processors

  9. Experimental Results – Running Time

  10. Experimental Results – Robustness • 20 PCs each receiving 100 protein molecules to dock • Some PCs randomly shut down • After P2P network stabilized fraction of molecules that could not be processed measures • Molecule docking failure rate almost equal to nodes failure rate as expected • No significant resource scheduling failure -> Robust

  11. Questions • Why are failed docking computations not rescheduled? • What type of network topology does Folding@Home use?

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