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Experimental Study of Adaptive Application-Sensitive Partitioning Strategies for SAMR Applications

Experimental Study of Adaptive Application-Sensitive Partitioning Strategies for SAMR Applications. Sumir Chandra, Johan Steensland, Manish Parashar The Applied Software Systems Laboratory Rutgers University (submitted to Super Computing 2001). Need for Adaptive Partitioning.

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Experimental Study of Adaptive Application-Sensitive Partitioning Strategies for SAMR Applications

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  1. Experimental Study of Adaptive Application-Sensitive Partitioning Strategies for SAMR Applications Sumir Chandra, Johan Steensland, Manish Parashar The Applied Software Systems Laboratory Rutgers University (submitted to Super Computing 2001)

  2. Need for Adaptive Partitioning • No single partitioning scheme performs the best for all types of applications and systems • Optimal partitioning technique depends on input parameters and application run-time 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. Characterizing Partitioner Behavior • 3 partitioners • Space-filling curve based partitioning (SFC) [from GrACE] • Geometric multi-level inverse space-filling curve partitioning with sequence partitioning (G-MISP+SP) [from Vampire] • p-way binary dissection inverse space-filling curve partitioning (pBD-ISP) [from Vampire] • SFC – good load balance, greater communication and data migration overheads, suited for moderate activity dynamics • G-MISP+SP – favors simple communication and speed over data migration, good load balance, computationally expensive • pBD-ISP – fast, low overheads and communication costs, average load balance, suited for greater communication states with lesser emphasis on load balance

  5. Characterizing Application State • RM3D – 3-D compressible turbulence application solving Richtmyer-Meshkov fingering instability • Application trace – 800 coarse level time-steps / 200 snap-shots • 128*32*32 base grid, 3 levels, regriding every 4 time-steps

  6. Application State (contd.)

  7. Experimental Results • Runs performed on IBM SP2 “Blue Horizon” • Measure application execution times for adaptive and individual runs for different number of processors Partitioner performance for RM3D application on 64 processors

  8. Experimental Results (contd.)

  9. Conclusions • Structure of adaptive grid hierarchy is used to characterize current state and determine partitioning requirements • Adaptive partitioning can improve application performance – for 64 processors, improvement is 27.2% over slowest partitioner • Future work • integrate application and system sensitive mechanisms • define policies to drive the partitioner recommender system

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