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DynaSoar A Scalable Architecture for High Performance AI Applications

DynaSoar A Scalable Architecture for High Performance AI Applications. Syed Enam-ur-Rehman Department of Computer Engineering Sir Syed University of Engineering & Technology. AI Trends & Applications. Large Simulations Behavioral / Social Modeling Control Systems Resource Management.

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DynaSoar A Scalable Architecture for High Performance AI Applications

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  1. DynaSoarA Scalable Architecture for High Performance AI Applications Syed Enam-ur-Rehman Department of Computer EngineeringSir Syed University of Engineering & Technology

  2. AI Trends & Applications • Large Simulations • Behavioral / Social Modeling • Control Systems • Resource Management

  3. Multi Agent Systems • Computation Intensive • Data Intensive • Communication Intensive

  4. Goals & Objectives • High Performance • Transparency • Single System Image • Lower cost to performance ratio

  5. DynaSoar Node 1 DynaSoar Node n Utility Server SK SK . . . RA Sch RA Sch Soar API Soar API Registration Monitoring Management INCM INCM INCM INCM INCM SK Soar Kernel RA Resource Analyzer Sch Scheduler INCM Inter-Node Communication Module DSAPI Distributed Soar API . . . DSAPI DSAPI Environment Environment EnvironmentNode 1 EnvironmentNode n The Architecture

  6. Soar • Soar Kernel • Core AI Engine • Agent Maintenance and Execution • Soar API • Wrapper to Soar Kernel • Abstraction

  7. Scheduler • Predective Task Scheduling • Load Balancing • Asymmetric Systems • Distributed Centralized

  8. Resource Analyzer • Specifications • Availability • Usage History

  9. INCM (Inter Node Communication Module) • Gateway • Request Dispatch • Request Block • Searching • Allocation Tables & Updating • Abundant I/O Channels

  10. DSAPI(Distributed Soar API) • Interface • Utilizes INCM • Abstraction • Transparency • SSI • Agent Creation • Agent Interaction

  11. Environment • User-space • Utilizes DSAPI • Single Entry Point • Distributed & Concentrated

  12. Utility Server • Monitoring • Configuration • Registration • Master Allocation Table • Backup

  13. Development Phases

  14. Scheduling Parameters

  15. Scheduling Policy If TRTO = 0 If TRTO > 0 For simulation purpose:

  16. Speculation

  17. Conclusion • Separate Execution of Soar & Environment • Uniform gain • Easier to implement Large AI Applications

  18. Enquiries Syed Enam-ur-Rehman (senam@ieee.org) Usman Azeem Usmani (usman@vetolimits.com) Nabeel Shaheen (nabeel@vetolimits.com) Qazi Raheel Akhtar (raheel@vetolimits.com )http://sourceforge.net/projects/dynasoar/

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