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Use of SPEEDES for BMDSsim

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Use of SPEEDES for BMDSsim

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    1. Use of SPEEDES for BMDSsim

    2. 2 Overview Metron SPEEDES Approach to BMDSsim: Clustering Summary

    3. 3 Metron, Inc.

    4. 4 SPEEDES Synchronous Parallel Environment for Emulation and Discrete-Event Simulation Powerful optimistic-processing parallel processing engine Developed, maintained, and distributed by Metron since 1996 Open source code downloadable to qualified users On-line documentation On-line change request system Primary users: MDWAR and related MDA projects IMDSE Conservative only Windows NT SPEEDES just works and lets them model C2BMC ABL Other SPEEDES-related efforts: Air Force Research Lab (Rome Labs) Rome Labs funded iterator improvement, incorporated in Version 2 Distributed Information Enterprise Modeling and Simulation (DIEMS) Parallel multiple-course-of-action SPEEDES enhancement NASA KSC Independent IV&V of the SPEEDES variant developed for JSIMS

    5. 5 Metron’s SPEEDES Team

    6. 6 SPEEDES Early Development and Modern Versions Chosen as framework for MDWAR in late 1996 Early beta versions concentrated on functionality rather than reliabilty Frequently buggy, undocumented, poor performing Version 1.0 (November 2000) Completed the Unified API Added the SPEEDES User’s Guide* Added the API Reference Manual* Much of the obsolete code was removed Version 2.0 (September 2001) Added object proxy attribute subscription Added automatic lazy re-evaluation General code optimization (size and speed)

    7. 7 Modern Versions of SPEEDES Version 2.1 (September 2003) Second port to NT: Ported to Microsoft Visual Studio Removed novel event queue design Resulted in net performance improvement General code optimization Reduced memory requirements Reduced executable size Fixed numerous Data Declaration Management bugs Added optimized conservative algorithm Version 2.2 (August 2004) Initial implementation of shared memory host router Standard Template Library (STL) containers RB_map, RB_list, RB_vector, RB_multimap All are significantly (4-10x) faster than current rollbackable containers Function with non-rollbackable STL algorithms library General performance improvements

    8. 8 Approach to BMDSsim: Clustering Clustering can lead to high performance federations Retains ability for easy debugging modes Can link up through shared memory or TCP/IP Design allows for MDWAR Standard Gateway (MSG) connections Elements could hook together in variety of fashions Optimistic: Full optimistic time management with rollbacks Includes the option of connecting through shared memory on the same machine or TCP/IP for those that are remote. Conservative: Linked through MSGs Playback: A element could be replaced by an MSG Playback for standalone testing/debugging Any combination of the above

    9. 9 How clustering works

    10. 10 Summary Main SPEEDES focus is and will be on stability, and reliability Performance has already been proven Continuing use in wargames provides rigorous test environment Mature set of tools help optimize performance, minimize overhead SPEEDES instrumentation MDWAR simulation instrumentation and analysis tools Rules of thumb Continuing improvement Changes for usability Reduction in memory and CPU footprint AFRL funded parallel course of action simulation (due March 2006) MDA can have confidence in high performance, low risk for BMDSsim

    11. Back-ups (Lessons Learned)

    12. 12 Lessons learned SPEEDES has been extraordinarily resilient Almost all performance problems have been due to improper modeling Framework bugs are now rare Significantly impacted development in the early (< v 0.8) years Proxy mechanism is solid but tightly couples models Use of proxy updates has decreased significantly Proxies use often indicates incorrect modeling Not communicating through message sets Unnecessary or excessive notifications Attribute subscription carries a small penalty Often used to simply unsubscribe totally to proxy updates

    13. 13 Lessons learned (cont) Performance tuning requires analysis tools Real time performance does not come for free Built up suite of analysis tools SPEEDES instrumentation has been extensive and varied MDWAR has many tools to analyze the instrumentation files Rules of thumb learned about modeling New APIs added to improve parallelism SPEEDES overhead is minimal (usually 10s of micro-seconds/event) Recent tests using MDWAR 5.0 (SPEEDES 2.1) on 1 node (optimistic) shows a ~15% framework overhead. BTW is within 20% of sequential on SPEEDES 2.1, should be 10-15% with 2.2 I/O is a killer. Data collection has a minor impact Biggest problem is making sure we collect enough

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