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RESULTS. Experimental Framework. Goal Simplicity of a trace based simulator Flexibility to model special predictors ( e.g., using data values) Trace driven with pipeline timing information Tracing Methodology: Detailed timing simulator with perfect branch predictor 50M uops per trace

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RESULTS

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Results

RESULTS


Experimental framework

Experimental Framework

  • Goal

    • Simplicity of a trace based simulator

    • Flexibility to model special predictors ( e.g., using data values)

  • Trace driven with pipeline timing information

  • Tracing Methodology:

    • Detailed timing simulator with perfect branch predictor

    • 50M uops per trace

    • Traces include pipeline behavior/timing, instruction address, uop type etc.


Experiments

Experiments

  • Workloads

    • 40 workloads selected from a large pool of applications

    • 5 classes: CLIENT 16, INT 6, MM 7, SERVER 5, WS 6

  • Metrics

    • Arithmetic average of MPPKI ( Misprediction Penalty per Kilo Instructions)

No secret workloads


Conditional predictor results

Conditional predictor results

  • #1

    • A. Seznec, A 64 Kbytes ISL-TAGE branch predictor, MPPKI 568

  • #2

    • Y. Ishii , K. Kuroyanagi, T. Sawada, M. Inaba, K. Hiraki, Revisiting Local History for Improving Fused Two-Level Branch Predictor, MPPKI 581

  • #3

    • D. Jimenez, OH-SNAP: Optimized Hybrid Scaled Neural Analog Predictor, MPPKI 598

  • #4

    • Y. Hu, D. Koppelman and L. Peng, Penalty-Sensitive L-TAGE Predictor, MPPKI 608

  • #5

    • G. Shi and M. Lipasti, Perceptron Branch Prediction with Separated Taken/Not-Taken Weight Tables, MPPKI 677


Indirect predictor results

Indirect predictor results

  • #1

    • A. Seznec, A 64-Kbytes ITTAGE indirect branch predictor, MPPKI 34.1

  • #2

    • Y. Ishii, T. Sawada, K. Kuroyanagi, M. Inaba, K. Hiraki, Bimode Cascading: Adaptive Rehashing for ITTAGE Indirect Branch Predictor, MPPKI 37.0

  • #3

    • N. Bhansali, C. Panirwala, H. Zhou, Exploring Correlation for Indirect Branch Prediction, MPPKI 51.6

  • #4

    • Daniel A. Jimenez, SNIP: Scaled Neural Indirect Predictor, MPPKI 52.9


Discussions

Discussions

  • From industry perspective

    • It is important!

      Perf gain from better branch predictors Branch prediction papers

      Total perf gain from uarch changes uarch papers

    • What do we need from researchers?

  • What’s next?


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