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

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Presentation transcript:

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

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

WW Domain Dominant Pathways 3

Molecular Dynamics 4

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

Scaling

Pathways not simple 8 Bowman et al PNAS 107

Want to Sample Rare Events 9

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

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

How does WQ enable AWE?

Heterogeneous Resources 13

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

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 sustained workers

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

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

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 , NIH 1R01 GM , NIH 7R01 AI Resources: – Notre Dame Center for Research Computing – Stanford Institute for Computation and Mathematical Engineering 18

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