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Performance and Scaling Effects of MD Simulations using NAMD 2.7 and 2.8

Performance and Scaling Effects of MD Simulations using NAMD 2.7 and 2.8. Grad OS Course Project Kevin Kastner Xueheng Hu. Introduction. Molecular Dynamics (MD) MD is extremely computationally intensive Primarily due to the sheer size of the system

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Performance and Scaling Effects of MD Simulations using NAMD 2.7 and 2.8

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  1. Performance and Scaling Effects of MD Simulations using NAMD 2.7 and 2.8 Grad OS Course Project Kevin Kastner Xueheng Hu

  2. Introduction • Molecular Dynamics (MD) • MD is extremely computationally intensive • Primarily due to the sheer size of the system • Large system simulation can potentially take thousands of years on a modern desktop • NAMD – Parallelized simulation tool for MD • Recent release is 2.8

  3. GPCR Simulation Example

  4. Summary of Work Completed • Performance Comparison: NAMD 2.7 vs 2.8 • Tested three different systems using each version, comparing efficiency of each • How different size/complexity of the systems affect the performance of NAMD • NAMD Scaling analysis • Force Field Comparison

  5. Performance Metrics • Performance Efficiency • Performance Efficiency per Core • Normalized Performance Efficiency per Core x: core set; base: 12

  6. Simulation Systems Octopamine Receptor, a GPCR 56824 atoms (b) DHFR-TS Fusion Protein 82026 atoms (c) Ubiquitin 7051 atoms

  7. Results - 57000 Atoms 57000 Atom Efficiency

  8. Results - 57000 Atoms 57000 Atom Efficiency per Core

  9. Results - 80000 Atoms 80000 Atom Efficiency

  10. Results - 80000 Atoms 80000 Atom Efficiency per Core

  11. Results - 7000 Atoms 7000 Atom Efficiency

  12. Results - 7000 Atoms 7000 Atom Efficiency per Core

  13. Results – NAMD Scaling Analysis Optimal Number of Cores Peak Performance

  14. Results - Force Field Comparison NAMD 2.7 – 57000 atoms NAMD 2.8 – 57000 atoms

  15. Summary of Results • Performance Difference • 57000 and 80000 atom: NAMD 2.8 was optimized for performance using larger core sets • 7000 atom: odd results, two possible reasons: • performance optimization only works for larger simulation systems • the performances for either version will start to increase again if giving enough cores and the efficiencies may potentially reverse once again • NAMD Scaling Analysis • Optimal Number of Cores • Peak Performance • Force Field Comparison • CHARMM vs AMBER

  16. Future Work • More test cases to obtain empirical data for performance boundaries • Deeper Analysis on Performance Differences • System Calls • Network Communications (We need to find out available tools for Kraken)

  17. Questions?

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