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Adaptive Partitioning. Sumir Chandra The Applied Software Systems Laboratory Rutgers University. ARMaDA Recommender. No single partitioning scheme performs the best for all types of applications and systems

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Adaptive partitioning

Adaptive Partitioning

Sumir Chandra

The Applied Software Systems Laboratory

Rutgers University


Armada recommender
ARMaDA Recommender

  • No single partitioning scheme performs the best for all types of applications and systems

  • Optimal partitioning technique depends on input parameters and application runtime state

  • Partitioning behavior characterized by the tuple {partitioner, application, computer system} (PAC)

  • PAC quality characterized by 5-component metric – communication, load imbalance, data migration, partitioning time, partitioning overhead

  • Octant approach characterizes application/system state

  • Adaptive meta-partitioner -> fully dynamic PAC



Rm 3d switching test
RM-3D Switching Test

  • Richtmyer-Meshkov fingering instability in 3 dimension

  • Application trace has 51 time-step iterations

  • RM-3D has more localized adaptation and lower activity dynamics

  • Depending on computer system, application RM-3D resides in octants I and III for most of its execution

  • Partitioning schemes pBD-ISP and G-MISP+SP are suited for these octants

  • Application trace -> Partitioner -> Output trace -> Simulator -> metric measurements



Rm 3d switching test contd1
RM-3D Switching Test (contd.)

Test Runs

  • CGD – complete run

  • pBD-ISP – complete run

  • CGD+pBD-ISP_load (for improved load balance)

    0 – 12 -> CGD 13 – 22 -> pBD-ISP

    23 – 26 -> CGD 27 – 36 -> pBD-ISP

    37 – 48 -> CGD 49 – 51 -> pBD-ISP

  • CGD+pBD-ISP_data (for reduced data migration)

    0 – 10 -> CGD 11 – 28 -> pBD-ISP

    29 – 34 -> CGD 35 – 51 -> pBD-ISP



Conclusions
Conclusions

  • YES !!! Experimental results conform to theoretical observations

  • Recommender systems in ARMaDA can result in performance optimization

  • Future work

    - more robust rule-set and switching policies

    - partitioner/hierarchy optimization at switch-points

    - integration of recommender engine within ARMaDA

    - partitioner and application characterization research to form policy rule base


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