Optimistic Parallel Discrete Event Simulations of Physical Systems using Reverse Computation

1. Optimistic Parallel Discrete Event Simulations ofPhysical Systems using Reverse Computation 06/01/2005

2. The Big Picture • Global Multi-Scale Kinetic Simulations of the Earth's Magnetosphere • Multi-physics; multi-scale • The goal: to understand how solar wind interacts with the Earth’s magnetosphere (space weather) • People • PDES simulation: lead by Richard Fujimoto and Kalyan Perumalla • Compiler optimization: lead by Pande Santosh • Physics modeling: lead by Homa Karimabadi @ Scibernet Inc. Georgia Institute of Technology

3. Outline • Motivation • Overview • The PIC model (Particle-in-Cell) • Implementation • Performance • Conclusion & Future work Georgia Institute of Technology

4. Optimistic Approach Why optimistic simulation? Synchronization in PDES: ensures LPs to process events in time stamp order • Conservative approach • Avoids violation of local causality constraint • Inefficient for applications with little lookahead (low “predictability”) • Plasma simulation highly dynamic, nearly-zero lookahead in certain regions Georgia Institute of Technology

5. Why reverse computation? Optimistic synchronization: Allows causality errors to happen, but uses a rollback mechanism to recover • State saving: saves state prior to computation; restores saved state • Plasma simulation has a lots of events → lots of memory! • Event computation cost is small compared to state saving overhead → significant overhead! • Reverse computation: rollback by performing the inverse of the event computation • Reduces both memory and time needed for state saving • Can be automated • Excellent match for large, high-performance models Georgia Institute of Technology

6. Outline • Motivation • Overview • The PIC model (Particle-in-Cell) • Implementation • Performance • Conclusion • Future work Georgia Institute of Technology

7. Particle-in-Cell (PIC) Model • Charging a spacecraft due to a periodical beam injection from its surface • Conceptually simple, yet sufficiently complex to capture characteristics of plasma dynamics • What are we interested in the simulation? • Charge on spacecraft surface • Plasma dynamics Georgia Institute of Technology

8. Challenges • Complicated interactions between physical entities: cells (modeled by LPs) and particles Cell1 Cell2 • Cell1 sends a particle to cell2 • Cell1 updates state and may “wake up” all its particles • Cell2 updates state and may “wake up” all its particles Georgia Institute of Technology

9. Add Add Add Add Move Move Move Move Particle Movement Illustrated Cell 1 Cell 2 Cell 3 Simulation Time Georgia Institute of Technology

10. Challenges cont.d • Memory requirement • Particle state Velocity, acceleration, position, direction, type, exit_time, lastmove_time, etc. • Cell state Center field, boundary fields, coordinates, particle queue Q: How to minimize the number of auxiliary bits for efficient reverse code? Georgia Institute of Technology

11. Challenges cont.d • Floating point arithmetic throughout simulation • Simple increment/decrement won’t work • Destructive operations are dominant • Particle’s exit_time is computed by solving a pair of quadratic equations → not reversible • “Wakeup” event → recompute all particle states • Delete operations in particle queue Georgia Institute of Technology

12. Our observations • “delete” in queue is the inverse of “insert” and vise versa • Particles never “disappear”; one deletion in a cell corresponds to an insertion in another cell • Partial particle state is carried over by msgs • No need to re-compute these state variables • Reversing physical states can resort to physics laws • No need for “brute-force” when reversing code • Application semantics is the magic for irreversible code Georgia Institute of Technology

13. Implementation example Shell::arrival( ParticleArrivalEvent *e ) { if( thiscell is active ) { update cell state; insert particle in cell; } else if ( e is abeam particle ) { activate cell; }}Shell::departure(ParticleDepartureEvent *e ) { if( particle bounced from right neighbor ){ bounce particle back; // no state change } else {update cell state; }}Shell::inject( ParticleInjectEvent *e ) {update cell state; insert beam particles;} Shell::undo_arrival( ParticleArrivalEvent *e ) { if ( cell was activated ) { undo_activate cell; } else if ( cell already active ) { delete particle in cell; undo_update cell state; }}Shell::undo_departure(ParticleDepartureEvent *e ) { if ( particle was bounced from right neighbor ) { undo_bounce particle; } else {undo_update cell state; }}Shell::undo_inject( ParticleInjectEvent *e ) {delete beam particles; undo_update cell state;} Georgia Institute of Technology

14. Add@ 20 Add@ 10 Move@ 30 Move@ 40 Move@ 5 Ion E E E Ion Reverse Computation Illustrated ? Reversex' = x-v.dt-½a.dt2v' = v-a.dtQ' = Q-q/c.w2a' = Q'.q/mDelParticleFromPQ() ForwardAddParticleToPQ() Q = Q'+q/c.w2a = Q.q/mv = v'+a.dtx = x'+v.dt+½a.dt2 … Cell 2 dt Cell 3 Simulation Time Georgia Institute of Technology

15. Experiment • Simulation engine: μsik (by Kalyan Perumalla) • General-purpose parallel/distributed sim engine • Supports multiple synchronization approaches • Hardware platform: • SMP machines running Red Hat Linux with a customized 2.4.18-10smp kernel • 8 PIII 550MHz Xeon processors sharing 4GB of memory Georgia Institute of Technology

16. Simulation configuration [1] Karimabadi, H, et al. A New Asynchronous Methodology for Modeling of Physical Systems: Breaking the Curse of Courant Condition. J. Computational Physics, 2005 • Normalized units • Simulate 7000 cells with 70 initially active cells • Spacecraft has a radius of 500 & cell has a width of 0.24 • Solar wind plasma initially loaded with uniformly distributed electrons and protons (30 of each per cell) • Injected positron beam has an energy of 10 keV with a period of 4ms Georgia Institute of Technology

17. Phase space comparison Time-stepped Sequential DES Georgia Institute of Technology

18. Validation by phase space comparison Sequential DES PDES Georgia Institute of Technology

19. PDES vs. sequential DES Georgia Institute of Technology

20. Events distribution Total Number of Committed Events Georgia Institute of Technology

21. Conclusions • Being the First to apply reverse computation to parallel simulations of physical systems • Propose application-level reverse computation • Encouraging results for 1D model, better performance expected for models with a higher dimension Georgia Institute of Technology

22. Ongoing and future work • Performance comparison with check-pointing approaches • More complex applications on larger-scale systems • Apply reverse computation to other domains of physics applications involving partial differential equations Georgia Institute of Technology

23. Any questions? Thank you! Georgia Institute of Technology