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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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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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