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The Charm++ Programming Model and NAMD

This presentation by Abhinav S. Bhatele from the University of Illinois at Urbana-Champaign delves into the Charm++ programming model, emphasizing its advantages in software engineering, dynamic mapping, and load balancing. Bhatele discusses the effectiveness of virtual processes, adaptive MPI, and performance tuning within the context of NAMD, a software for nanoscale molecular dynamics simulations. Insights into hybrid decomposition, optimization strategies, and performance analysis are presented, illustrating how these techniques enhance simulation efficiency and tackle complex computational challenges in the field.

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The Charm++ Programming Model and NAMD

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  1. The Charm++ Programming Model and NAMD Abhinav S Bhatele Department of Computer Science University of Illinois at Urbana-Champaign http://charm.cs.uiuc.edu E-mail: bhatele@illinois.edu

  2. University of Illinois Urbana-Champaign, Illinois, USA Abhinav S Bhatele

  3. Processor Virtualization Programmer: Decomposes the computation into objects Runtime: Maps the computation on to the processors User View System View Abhinav S Bhatele

  4. Benefits of the Charm++ Model • Software Engineering • Number of VPs independent of physical processors • Different sets of VPs for different computations • Dynamic Mapping • Load balancing • Change the set of processors used • Message-driven execution • Compositionality • Predictability Abhinav S Bhatele

  5. Charm++ and CSE Applications Gordon Bell Award, SC 2002 Fine-grained CPAIMD Cosmological Simulations Abhinav S Bhatele

  6. Adaptive MPI (AMPI) MPI “Processes” - Implemented as virtual “processes” (light-weight user level migratable threads) Abhinav S Bhatele

  7. Load Balancing • Based on Principle of Persistence • Runtime instrumentation • Measures communication volume and computation time • Measurement based load balancers • Use the instrumented data-base periodically to make new decisions • Many alternative strategies can use the database • Centralized vs distributed • Greedy improvements vs complete reassignments • Taking communication into account Abhinav S Bhatele

  8. NAMD • NAnoscale Molecular Dynamics • Simulates the life of bio-molecules • Simulation window broken down into a large number of time steps (typically 1 fs each) • Forces on each atom calculated every step • Positions and velocities updated and atoms migrated to their new positions Abhinav S Bhatele

  9. Parallelization of NAMD • Hybrid Decomposition Abhinav S Bhatele

  10. Control Flow Abhinav S Bhatele

  11. Non-bonded Work Communication PME Bonded Work Integration Abhinav S Bhatele

  12. Optimizations on MareNostrum • Fixes to the CVS version • Crash due to a xlc compiler bug • Performance analysis • Using Projections performance analysis tool • Performance tuning • Configuration parameters for NAMD to enable high parallelization Abhinav S Bhatele

  13. Performance Tuning • Increasing the number of objects • twoAway{X, Y, Z} options • Choice of PME implementation • Slab (1D) vs. Pencil (2D) decomposition • Offloading processors doing PME • Use of spanning tree for communication Abhinav S Bhatele

  14. DD on MareNostrum Abhinav S Bhatele

  15. DD with Slab PME Abhinav S Bhatele

  16. DD with Pencil PME Abhinav S Bhatele

  17. Problem? 1.2 ms Abhinav S Bhatele

  18. Abhinav S Bhatele

  19. Nucleosome on MareNostrum Abhinav S Bhatele

  20. Membrane on MareNostrum Abhinav S Bhatele

  21. Similar Problem? Bandwidth on Myrinet: 250 MB/s Expected Latency for 6-10 KB messages < 0.1 ms 16 ms Abhinav S Bhatele

  22. Optimizations on the Xe Cluster • Getting the CVS version running (over mpi) • Trying a development version of charm • Runs directly over Infiniband (ibverbs) • Currently unstable Abhinav S Bhatele

  23. DD on Xeon Cluster Abhinav S Bhatele

  24. Thank You!

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