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Charm++ Load Balancing Framework

Charm++ Load Balancing Framework. Gengbin Zheng gzheng@uiuc.edu Parallel Programming Laboratory Department of Computer Science University of Illinois at Urbana-Champaign http://charm.cs.uiuc.edu. Motivation. Irregular or dynamic applications Initial static load balancing

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Charm++ Load Balancing Framework

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  1. Charm++ Load Balancing Framework Gengbin Zheng gzheng@uiuc.edu Parallel Programming Laboratory Department of Computer Science University of Illinois at Urbana-Champaign http://charm.cs.uiuc.edu

  2. Motivation • Irregular or dynamic applications • Initial static load balancing • Application behaviors change dynamically • Difficult to implement with good parallel efficiency • Versatile, automatic load balancers • Application independent • No/little user effort is needed in load balance • Based on Charm++ and Adaptive MPI

  3. Quantum Chemistry (QM/MM) Protein Folding Molecular Dynamics Computational Cosmology Crack Propagation Parallel Objects, Adaptive Runtime System Libraries and Tools Space-time meshes Dendritic Growth Rocket Simulation

  4. Load Balancing in Charm++ • Viewing an application as a collection of communicating objects • Object migration as mechanism for adjusting load • Measurement based strategy • Principle ofpersistent computation and communication structure. • Instrument cpu usage and communication • Overload vs. underload processor

  5. Load Balancing – graph partitioning Weighted object graph in view of Load Balance mapping of objects LB View Charm++ PE

  6. Load Balancing Framework LB Framework

  7. Centralized Object load data are sent to processor 0 Integrate to a complete object graph Migration decision is broadcasted from processor 0 Global barrier Distributed Load balancing among neighboring processors Build partial object graph Migration decision is sent to its neighbors No global barrier Centralized vs. Distributed Load Balancing

  8. Load Balancing Strategies

  9. Strategy Example - GreedyCommLB • Greedy algorithm • Put the heaviest object to the most underloaded processor • Object load is its cpu load plus comm cost • Communication cost is computed as α+βm

  10. Strategy Example - GreedyCommLB

  11. Strategy Example - GreedyCommLB

  12. Strategy Example - GreedyCommLB

  13. Comparison of Strategies Jacobi1D program with 2048 chares on 64 pes and 10240 chares on 1024 pes

  14. Comparison of Strategies NAMD atpase Benchmark 327506 atoms Number of chares:31811 migratable:31107

  15. User Interfaces • Fully automatic load balancing • Nothing needs to be changed in application code • Load balancing happens periodically and transparently • +LBPeriod to control the load balancing interval • User controlled load balancing • Insert AtSync() calls at places ready for load balancing (hint) • LB pass control back to ResumeFromSync() after migration finishes

  16. Migrating Objects • Moving data • Runtime packs object data into a message and send to its destination • Runtime unpacks the data and creates object • User needs to write pup function for packing/unpacking object data

  17. Compiler Interface • Link time options • -module: Link load balancers as modules • Link multiple modules into binary • Runtime options • +balancer: Choose to invoke a load balancer • Can have multiple load balancers • +balancer GreedyCommLB +balancer RefineLB

  18. NAMD case study • Molecular dynamics • Atoms move slowly • Initial load balancing can be as simple as round-robin • Load balancing is only needed for once for a while, typically once every thousand steps • Greedy balancer followed by Refine strategy

  19. Load Balancing Steps Regular Timesteps Detailed, aggressive Load Balancing Instrumented Timesteps Refinement Load Balancing

  20. Load Balancing Aggressive Load Balancing Refinement Load Balancing Processor Utilization against Time on (a) 128 (b) 1024 processors On 128 processor, a single load balancing step suffices, but On 1024 processors, we need a “refinement” step.

  21. Some overloaded processors Processor Utilization across processors after (a) greedy load balancing and (b) refining Note that the underloaded processors are left underloaded (as they don’t impact perforamnce);refinement deals only with the overloaded ones

  22. Profile view of a 3000 processor run of NAMD (White shows idle time)

  23. Load Balance Research with Blue Gene • Centralized load balancer • Bottleneck for communication on processor 0 • Memory constraint • Fully distributed load balancer • Neighborhood balancing • Without global load information • Hierarchical distributed load balancer • Divide into processor groups • Different strategies at each level

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