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Hybrid Parallel Data Structure

This project demonstrates a hybrid parallel data structure that combines local linearizability and strong linearizability.

Idea

  • Local shards reduce lock contention and improve performance.
  • A global counter handles critical strongly consistent updates.
  • Hybrid operations update both local and global state.

Files

  • HybridDataStructure.py – basic Python demo.
  • hybrid_scalability_benchmark.py – Python scalability/performance test.
  • hybrid_consistency_metrics_test.py – Python consistency gap and lock metrics test.
  • Main.java – Java implementation/test for the hybrid parallel queue.
  • MppRunner.java – Java runner file.
  • MppRunnerCode.java – additional Java runner/code version.
  • Hybrid Parallel Data Structure.pdf – project report.
  • mpp.out – output/results file.

Metrics

The project measures:

  • Execution time
  • Throughput
  • Lock wait time
  • Write conflicts
  • Global counter
  • Local shard sum
  • Consistency gap

Conclusion

The project shows the trade-off between scalability and consistency. Sharding improves performance, but with more threads, the gap between local and global state can increase.