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Adaptive Partitioning

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

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  1. Adaptive Partitioning Sumir Chandra The Applied Software Systems Laboratory Rutgers University

  2. 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

  3. Dynamic Characterization

  4. 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

  5. RM-3D Switching Test (contd.)

  6. 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

  7. RM-3D Switching Test (contd.)

  8. 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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