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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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