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This paper presents a trace-based simulator to evaluate the performance of branch predictors, focusing on simplicity and flexibility in modeling special predictors. The simulator uses pipeline timing information and includes detailed timing simulations with a perfect branch predictor. Experimental results are provided with 40 selected workloads and performance metrics. The discussions highlight the importance of better branch predictors and the need for further research.

hellerjames
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RESULTS

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  1. RESULTS

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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?

  7. Detailed Results

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