Energy-efficient branch prediction with compiler-guided history stack

Proceedings -Design, Automation and Test in Europe, DATE

Volumn , Issue , 2012, Pages 449-454

  Tan, Mingxing ^a   Liu, Xianhua ^a   Xie, Zichao ^a   Tong, Dong ^a   Cheng, Xu ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BRANCH MISPREDICTIONS; BRANCH PREDICTION; BRANCH PREDICTORS; ENERGY EFFICIENT; GSHARE PREDICTOR; INSTRUCTION LEVEL PARALLELISM; MODERN PROCESSORS; PERCEPTRON; PERFORMANCE IMPROVEMENTS; PREDICTION ACCURACY; RUNTIMES; STACK STRUCTURE;

ENERGY EFFICIENCY; EXHIBITIONS; FORECASTING;

PROGRAM COMPILERS;

EID: 84862059661     PISSN: 15301591     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. ARM. Cortex-a9 technical reference manual, version r3p0, 2008.
2. A. Baniasadi and A. Moshovos. Sepas: A highly accurate energy-efficient branch predictor. In ISLPED'04, pages 38-43, 2004. (Pubitemid 40454679)
3. S. Borkar and A. A. Chien. The future of microprocessors. Communications of the ACM, vol.54(5):67-77, 2011.
4. D. Burger and T. M. Austin. The simplescalar tool set, version 2.0. SIGARCH Comput. Archit. News, vol.25(3):13-25, 1997.
5. I.-C. K. Chen, J. T Coffey, and T. N. Mudge. Analysis of branch prediction via data compression. In ASPLOS-VII, pages 272-281,1996.
6. B. Choi, L. Porter, and D. M. Tullsen. Accurate branch prediction for short threads. In ASPLOS-XIII, pages 125-134,2008.
7. M. Evers, S. J. Patel, R. S. Chappell, and Y. N. Patt. An analysis of correlation and predictability: what makes two-level branch predictors work. In ISCA-25, pages 52-61, 1998.
8. F. Gao and S. Sair. Exploiting intra-function correlation with the global history stack. In SAMOS-5, pages 213-223, 2005.
9. GCC. The gnu compiler collection. http://gcc.gnu.org/.
10. S. Gochman et al. The intel pentium m processor: Microarchitecture and performance. Intel Technology Journal, vol.7(2), 2003.
11. R. Hameed et al. Understanding sources of inefficiency in general-purpose chips. In ISCA-37, pages 37-47, 2010.
12. D. A. Jiménez. Piecewise linear branch prediction. In ISCA-32, pages 382-393, 2005.
13. D. A. Jiménez and e. Lin. Dynamic branch prediction with perceptrons. In HPCA-7, pages 197-201, 2001.
14. S. McFarling. Combining branch predictors. Technical Note TN-36, Digital. Equipment Corporation Western Research Laboratories, 1993.
15. P. Michaud, A. Seznec, and R. Uhlig. Trading contlict and capacity aliasing in conditional branch predictors. In ISCA-24, 1997.
16. L. Porter and D. M. Tullsen. Creating artificial global history to improve branch prediction accuracy. In ICS-23, pages 266-275, 2009.
17. Y. Sazeide, A. Moustakas, K. Constantinides, and M. Kleanthous. The significance of affectors and affectees correlations for branch prediction. In HiPEAC-3, pages 243-257, 2008.
18. A. Seznec. Analysis of the o-geometric history length branch predictor. In ISCA-32, pages 394-405, 2005.
19. A. Seznec. The l-tage branch predictor. Journal of Instruction Level Parallelism, vol.9, 2007.
20. A. Seznec. A new case for the tage branch predictor. In MICRO-44, 2011.
21. K. Skadron, P. S. Ahuja, M. Martonosi, and D. W. Clark. Improving prediction for procedure returns with return-address-stack repair mechanisms. In MICRO-31, pages 259-271, 1998.
22. SPEC. Standard performance evaluation corporation. http://www.spec.org.
23. V. C. Sreedhar, G. R. Gao, and Y-F. Lee. Identifying loops using dj graphs. ACM Transactions on Programming Languages and Systems, vol.18(6):649-658, 1996. (Pubitemid 126418663)
24. T.-Y. Yeh and Y. N. Patt. Two-level adaptive training branch prediction. In MICRO-24, pages 51-61, 1991.