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Enhancing Performance with Data Value Prediction: PowerPC 620+ Speedup Study

Learn about the limits of ILP in instruction-level parallelism due to control and data flow dependencies. Explore the sources and uses of value prediction for improving accuracy and achieving speedups on PowerPC 620 and beyond. Discover the potential benefits and future developments in prediction models and parallel execution units.

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Enhancing Performance with Data Value Prediction: PowerPC 620+ Speedup Study

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Presentation Transcript

  1. Data value prediction Bas van der Tol

  2. Limits to ILP Instruction Level Parallelism is limited by • Control flow • Data flow: true dependencies

  3. Types of Speculative Execution

  4. Sources of predicatable data • Data redundancy • Error-checking • Program constants • Virtual function calls • Glue code • Call-subgraph identities • Register spill code

  5. Register value locality

  6. Value Prediction Unit

  7. VPT Hit rate sensitivity to Size

  8. Example use of Value Prediction

  9. Penalties Penalties • Misprediction penalty • Structural hazards, both on correct and mispredictions

  10. Configurations used for experiments

  11. PowerPC 620 Speedup

  12. PowerPC 620+ Speedup

  13. Infinite Machine Model Speedup

  14. Data cache vs. Value Prediction

  15. Improving Prediction Accuracy • Last Value Prediction • Stride Prediction • Finite Context Method Predictors (fcm)

  16. Finite Context Models

  17. Prediction Success

  18. Conclusions • Data value prediction increases performance by 5% on PowerPC 620 • A performance gain of 23% is possible Future Developments • More parallel execution units • Better prediction models

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