1. Massively Parallel Molecular Dynamics Using Adaptive Weighted Ensemble Badi’ Abdul-Wahid PI: Jesús A. Izaguirre CCL Workshop 2013

2. Proteins Active β-2 Adrenergic Receptor bound to G-Protein (3sn6) Proteosome (1l5q) HIV-1 Protease with drug (1hxb) Antibody (1igt)

3. WW Domain Dominant Pathways 3

4. Molecular Dynamics 4

5. Many Complex Pathways 5 Lane et al. 2013 Curr. Opin. Struct. Biol. 23

6. Folding@Work

7. Folding@Work Scaling

8. Pathways not simple 8 Bowman et al. 2010 PNAS 107

9. Want to Sample Rare Events 9

10. Accelerated Weighted Ensemble 10 10,000s of walkers 100 - 1000 of iterations Resampling: ensures correct statistics - walkers are weighted - walkers are merged/split Colors allow rates to be calculated

11. AWE-WQ 11 Merge, Split, Reweight Run Walkers Submit BarrierBarrier BarrierBarrier

12. How does WQ enable AWE?

13. Heterogeneous Resources 13

14. Elasticity for Performance and Fault-Tolerance Without Task ReplicationWith Task Replication 14

15. Some Numbers 3 million+ tasks executed 600+ years of CPU time 8 months wall time Aggregate 1 μs/day achievable 1.5+ ms simulation time 2500+ sustained workers

16. Study of WW using AWE-WQ Long Trajectory (input) AWE-WQ Folding Rate (output) 16

17. Future Work Support for explicit solvent simulations Improved cell discovery and partitioning Incorporate improvements to Work Queue – GPU scheduling – very tricky! – Scheduling of multicore programs – Hierarchical Work Queue

18. Acknowledgements Lab – Prof. Jesús A. Izaguirre – Dr. Chris Sweet – Haoyun Feng – Kevin Kastner – Yong Hwan Kim – Ronald Nowling – James Sweet Collaborators: – Prof. Douglas Thain – Prof. Eric Darve (Stanford) – Dr. Ronan Costaouec (Stanford) – Dinesh Rajan – Li Yu Funding: – NSF CCF-1018570, NIH 1R01 GM101935-01, NIH 7R01 AI039071. Resources: – Notre Dame Center for Research Computing – Stanford Institute for Computation and Mathematical Engineering 18

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