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by Timothy H. Heil, Zak Smith, J. E. Smith
http://web.demigod.org/~zak/research/heil-micro-32.ps
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Abstract:

Branch predictors typically use combinations of branch PC bits and branch histories to make predictions. Recent improvements in branch predictors have come from reducing the effect of interference, i.e. multiple branches mapping to the same table entries. In contrast, the branch difference predictor (BDP) uses data values as additional information to improve the accuracy of conditional branch predictors. The BDP maintains a history of differences between branch source register operands, and feeds these into the prediction process. An important component of the BDP is a rare event predictor (REP) which reduces learning time and table interference. An REP is a cache-like structure designed to store patterns whose predictions differ from the norm. Initially, ideal interference-free predictors are evaluated to determine how data values improve correlation. Next, execution driven simulations of complete designs realize this potential. The BDP reduces the misprediction rate of five SPEC95 integer benchmarks by up to 33 % compared to gshare and by up to 15 % compared to Bi-Mode predictors. 1.

Citations

1253 The Simplescalar toolset, version 2.0 – Burger, Austin - 1997
521 Combining branch predictors – McFarling - 1993
365 A Study of Branch Prediction Strategies – Smith - 1981
246 Exceeding the Dataflow Limit via Value Prediction – Lipasti, Shen - 1996
227 A Comparison of Dynamic Branch Predictors that Use Two Levels of Branch History – Yeh, Patt - 1993
221 The predictability of data values – Sazeides, Smith - 1997
172 Highly Accurate Data Value Prediction using Hybrid Predictors – Wang, Franklin - 1997
100 Implementing the ppm data compression scheme,” in – Moffat - 1990
95 The YAGS branch prediction scheme – Eden, Mudge - 1998
86 The Bi-Mode Branch Predictor – Lee, Chen, et al. - 1997
83 Streamlining Interoperation Memory Communication via Data Dependence Prediction – Moshovos, Sohi - 1997
72 Dynamic Path-Based Branch Correlation – Nair - 1995
65 Analysis of branch prediction via data compression – Chen, Coffey, et al. - 1996
63 Dynamic History-Length Fitting: A third level of adaptivity for branch prediction – Juan, Sanjeevan, et al. - 1998
59 Correlation and Aliasing in Dynamic Branch Predictors – Sechrest, Lee, et al. - 1996
55 Alternative implementations of hybrid branch predictors – Chang, Hao, et al. - 1995
41 Efficacy and Performance Impact of Value Prediction – Rychlik, Faistl, et al. - 1998
39 Improving CC-NUMA Performance Using Instruction-Based Prediction – Kaxiras, Goodman - 1999
38 Dataflow Analysis of Branch Mispredictions and Its Application to Early Resolution of Branch Outcomes – Farcy, Temam, et al. - 1998
35 Variable length path branch prediction – Stark, Evers, et al. - 1998
25 Architectural support for compiler-synthesized dynamic branch prediction strategies: Rationale and initial results – August, Connors, et al. - 1997
23 Control-flow speculation through value prediction for superscalar processors – González, González - 1999
17 Predicting Indirect Branches via Data Compression – Kalamatianos, Kaeli - 1998
13 Trading Conflict and Capacity Aliasing – Michaud, Seznec, et al. - 1997
5 Difference Prediction and the Rare Event Predictor – Heil, “Branch - 1999
1 Intel Discloses New IA-64 Features," Microprocessor Report – Gwenmap - 1999