1. EE8365/CS8203 ADVANCED COMPUTER ARCHITECTURE A Survey on BRANCH PREDICTION METHODOLOGY By, Baris Mustafa Kazar Resit Sendag

2. Outlines Introduction Static Branch Prediction Schemes: A Brief View Dynamic Branch Prediction –One-Bit Scheme:BHT –To provide target instructions quickly: BTBs –Bimodal Branch Prediction (Two-Bit Prediction Scheme) –Two-level Branch Prediction Global History Schemes: GAg, GAs, GAp Per-Address History Schemes: PAg, PAs, PAp Per-Set Address Schemes: SAg, SAs, Sap –Correlation Branch Prediction –More on Global Branch Prediction: Gselect and Gshare –Hybrid Branch Predictors

3. Outlines (cont.) –Hybrid Branch Predictors Where did the idea come from? Branch Classification An alternative Selection Mechanism –Reducing PHT Interference Bi-Mode Branch Predictor The Agree Predictor The Skewed Branch Predictor The YAGS Branch Prediction Scheme Concluding Remarks

4. Introduction Pipeline flushes due to branch mispredictions is one of the most serious problems facing the designer of a deeply pipelined, super- scalar processor. Many branch predictors have been proposed to alleviate this problem… compile-time schemes (static scheduling) Focus on hardware-based prediction schemes (dynamic scheduling)

5. Static Branch Prediction Schemes: A Brief View Program behavior (i.e. branch direction) Profile information collected Some schemes: ☼ Always not taken, ☼ Always Taken, ☼ Op-code Based, ☼ Backward Taken and Forward not Taken.

6. Dynamic Prediction Schemes (DPS) One-Bit Scheme –Branch Prediction Buffer or Branch History Table (BHT) –indexed by lower bits of the branch instruction address –prediction bit To provide target instructions quickly: BTBs –Lee and Smith (1984) –Special instruction cache designed to store the target instructions

7. DPS (cont.) Two-Bit Prediction Scheme –Bimodal (usually taken or usually not taken) –2-bit Saturating Counter, Smith-1981 Two-level Branch Predictors –Yeh and Patt, 1991 –correlating predictors –First-level is the history of the last k branches encountered. –Second-level is the history of branch behavior of the last j occurences of that unique pattern of the last k branches. –Branch History Register and Pattern History Table (PHT) –Run-time collection of the history –Performs better than the other schemes given previously (up to97%)

8. Two-Level Branch Predictors (cont.) Two-level Predictors are classified into 3 classes(Yeh and Patt, 1993) Global History Schemes (GAg, GAs, GAp ) –The first-level branch history is the actual last k branches. –Only one global history register (GHR) –updated with the results from all branches

9. Two-Level Branch Predictors (cont.) Per-address History Schemes –Local Branch Prediction –The-first level history refers to the last k occurences of the same branch instruction –The branch prediction is independent of other branches’ execution history

10. Two-Level Branch Predictors (cont.) Per-set History Schemes –The first-level history means the last k occurences of the branch instructions from the same sub-set. –The set attribute –The prediction is influenced by the other branches in the same set

11. DPS (cont.) Comparison results for Two-level Branch Prediction Classes –Comparison is made upon the performance and cost effectiveness –Global History Schemes performs better on integer programs –Per-address history schemes performs better on floating point prog. –PAs is the most cost effective among low-cost schemes Correlation Branch Prediction –Pan, So, and Rahmeh, 1992 –GAp and GAs

12. DPS (cont.) More on Global Branch Prediction –Local Branch Prediction: history of each branch independently –Global Branch Prediction: combined history of all recent branches –Gselect: Global Predictor with Index Selection, Pan, So, Rahmeh, 1992. PHT is indexed by concatenations of global history and branch address performs better than either bimodal or global prediction –Gshare: Global Predictor with Index Sharing, McFarling 1993. PHT is indexed by XOR of global history and branch address

13. Hybrid Predictors : Combining Branch Predictors McFarling 1993 Different prediction schemes have different advantages Combined Predictor: Bimodal and Gshare 2 bit-counter is used to select one of the predictor performs always better than either predictor alone 98.1% vs 97.1% The idea of combining predictors was introduced first time.

14. Hybrid Predictors : Branch Classification Chang, Hao, Yeh, and Patt, 1994 Partitions a program’s branches into sets or branch classes Classes are based on run-time and compile-time info. Associates each branch class with the most suitable predictor 2-bit counter is used to select the branch predictor

15. An Alternative Selection Mechanism Single-scheme predictors and selection mechanisms 2-level Selector, Chang, Hao, and Patt, 1995 The concept of Two-level Branch Prediction is embodied. The performance of 2-level BPS(Branch Predictor Selector) is shown to be better than 2-bit counter BPS mechanism of McFarling

16. Reducing Pattern History Table Interference The main problem, which reduces the prediction rate in the global schemes is aliasing. Neutral aliasing- no mispredictions Destructive aliasing-misprediction

17. Reducing PHT Interference: The Agree Predictor Sprangle, Chappel, Alsup, Patt, 1997 Assigns a biasing bit to each branch in BTB The PHT info is changed with the bias bit. Hopes highly biased behavior of the branches is seen the first time a branch is introduced into the BT. Neutral aliasing

18. Reducing PHT Interference: Bi-Mode Predictors Lee, Chen, Mudge, 1997 Tries to replace destructive aliasing with neutral aliasing It splits the PHT table into even parts choice PHT direction PHTs: Taken and Not taken Xored indexing of direction PHTs

19. Reducing PHT Interference: The Skewed Branch Predictor Michaud, Seznec, Uhlig, 1997 Lack of Associativity in PHT conflict aliasing, rather than capacity set associative PHT? (tags, etc) Skewing Function splits PHT into 3 banks uses unique hashing function per bank majority vote partial updating of the banks

20. Reducing PHT Interference: The YAGS Branch Predictor Eden, Mudge, 1998 Yet Another Global Scheme (YAGS) Combines the strong points of several previous schemes introduces tags into the PHT that allows it to be reduced without sacrificing key branch outcome information. The size reduction more than offsets the cost of the tags. Gives better prediction accuracy for the SPEC95 benchmark suite than several leading prediction schemes, for the same cost.

21. Conclusions The Branch Prediction Methodology is studied. 2-level Branch Prediction was the most important step on the topic. Hybrid predictors, combining the advantages of the single- predictors are the most effective ones in branch prediction The selection of the predictors in the Hybrid predictors requires a good study of branch behavior and depends to a great extend upon the programs. Branch classification could be a promising method for Hybrid predictors. Using 2-level BPS gives better performance than the 2-bit BPS

22. Conclusions (cont.) Bi-Mode and Agree predictors that suggest splitting of the PHT into two branch streams have done a good job in reducing the aliasing in global schemes. YAGS scheme further reduces the aliasing by combining the strong points of previous schemes