improving branch prediction accuracy via effective source information

113 Pages·2008·0.61 MB·English

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Electronic Theses and Dissertations, 2004-2019
2008
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Hongliang Gao
University of Central Florida
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Gao, Hongliang, "Improving Branch Prediction Accuracy Via Effective Source Information And Prediction
Algorithms" (2008). Electronic Theses and Dissertations, 2004-2019. 3510.
https://stars.library.ucf.edu/etd/3510
IMPROVING BRANCH PREDICTION ACCURACY VIA EFFECTIVE
SOURCE INFORMATION
AND PREDICTION ALGORITHMS

by

HONGLIANG GAO
B.E. Beijing University of Aeronautics and Astronautics, 2001
M.S. University of Central Florida, 2005

A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the School of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
at the University of Central Florida
Orlando, Florida

Summer Term
2008

Major Professor:
Huiyang Zhou
© 2008 Hongliang Gao
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ABSTRACT
Modern superscalar processors rely on branch predictors to sustain a high instruction
fetch throughput. Given the trend of deep pipelines and large instruction windows, a branch
misprediction will incur a large performance penalty and result in a significant amount of energy
wasted by the instructions along wrong paths. With their critical role in high performance
processors, there has been extensive research on branch predictors to improve the prediction
accuracy.
Conceptually a dynamic branch prediction scheme includes three major components: a
source, an information processor, and a predictor. Traditional works mainly focus on the
algorithm for the predictor. In this dissertation, besides novel prediction algorithms, we
investigate other components and develop untraditional ways to improve the prediction accuracy.
First, we propose an adaptive information processing method to dynamically extract the most
effective inputs to maximize the correlation to be exploited by the predictor. Second, we propose
a new prediction algorithm, which improves the Prediction by Partial Matching (PPM) algorithm
by selectively combining multiple partial matches. The PPM algorithm was previously
considered optimal and has been used to derive the upper limit of branch prediction accuracy.
Our proposed algorithm achieves higher prediction accuracy than PPM and can be implemented
in realistic hardware budget. Third, we discover a new locality existing between the address of
producer loads and the outcomes of their consumer branches. We study this address-branch
correlation in detail and propose a branch predictor to explore this correlation for long-latency
and hard-to-predict branches, which existing branch predictors fail to predict accurately.
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To my mother and father

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ACKNOWLEDGEMENTS
This dissertation would not have been possible without the help and support of a number
of people. First of all, I would like to express my deepest gratitude to my esteemed advisor, Dr.
Huiyang Zhou. We joined University of Central Florida in the same year. I feel extremely lucky
and thankful to be his first PhD student. His encouragement, advice, mentoring and patience
have been very helpful to my research. I also want to thank my other dissertation committee
members, Dr. Mark Heinrich, Dr. Ronald F. DeMara and Dr. Liqiang Ni, for spending their time
to review the manuscript and providing valuable suggestions.
I am fortunate to work within a group of talented students, Yi Ma, Martin Dimitrov and
Jingfei Kong. I have enjoyed every moment that we have worked together. I am very grateful for
inspiring discussions with all of them. I want especially to thank Yi, who is my best friend and
has encouraged and helped me to go through all the difficult times during these five years. My
enormous debt of gratitude goes to Martin, who proof-read my dissertation and provided many
suggestions to help me improve it.
My graduate studies would not have been the same without my friends in Orlando. My
sincere gratitude goes to Yugang Min, Yixiao Yang, Baiyun Chen, Lifang Lou, Mengqian Chen,
Fuyu Liu, Min Li, Rui Peng, Yifang Gao, Hua Zhang, Li Miao, Feng Lv, Guoqiang Wang and all
members of the Chinese Culture Club of UCF. I appreciate their friendship and I have had a lot
of fun with them.
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Finally, I am deeply indebted to my mother Zelan He, my father Changquan Gao, my
sister Hongying Gao and my girlfriend Adan Niu. Their love and understanding have always
been the strongest support to me.
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TABLE OF CONTENTS
LIST OF FIGURES.......................................................................................................................xi
LIST OF TABLES.......................................................................................................................xiv
CHAPTER 1. INTRODUCTION..............................................................................................1
1.1. A conceptual system model for branch prediction.........................................................2
1.2. Our solutions to improve branch prediction accuracy....................................................2
1.3. Contributions...................................................................................................................3
CHAPTER 2. BACKGROUND................................................................................................5
2.1. History of Branch Prediction..........................................................................................5
2.2. Branch Prediction Schemes............................................................................................6
2.2.1. Bimodal Predictor...................................................................................................6
2.2.2. Two-level Predictor................................................................................................6
2.2.3. gshare Predictor......................................................................................................7
2.2.4. Perceptron Predictor................................................................................................7
2.2.5. O-GEHL Predictor..................................................................................................8
2.2.6. TAGE Predictor......................................................................................................8
2.3. Schemes to Alleviate Branch Misprediction Costs.........................................................9
CHAPTER 3. ADAPTIVE INFORMATION PROCESSING................................................11
3.1. Exploiting Correlation with Perceptron Branch Predictors..........................................11
3.2. Enhancing Branch Correlation Using Adaptive Information Processing.....................14
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3.2.1. Profile-directed Adaptation...................................................................................15
3.2.2. Correlation-directed Adaptation...........................................................................17
3.3. Overall Branch Predictor Structure...............................................................................18
3.4. Results and Analysis.....................................................................................................21
3.4.1. Performance for CBP-1 traces..............................................................................21
3.4.2. Performance for SPEC2000 Benchmarks.............................................................23
3.5. Summary.......................................................................................................................24
CHAPTER 4. PREDICTION BY COMBINING MULTIPLE PARTIAL MATCHES........26
4.1. Branch Prediction by Combining Multiple Matches....................................................28
4.1.1. Modeling PPM......................................................................................................28
4.1.2. A Study on PPM for Branch Prediction................................................................29
4.1.3. Prediction by Combining Multiple Partial Matches (PMPM)..............................32
4.1.4. Pushing the Limit of Branch Prediction Accuracy using PMPM.........................33
4.2. Idealistic Adaptive-PMPM Predictors..........................................................................36
4.2.1. Predictor Structure................................................................................................37
4.2.2. Prediction Policy...................................................................................................39
4.2.3. Update Policy........................................................................................................39
4.2.4. Results...................................................................................................................40
4.3. Realistic PMPM Predictors...........................................................................................41
4.3.1. Predictor Structure................................................................................................41
4.3.2. Prediction Policy...................................................................................................43
4.3.3. Update Policy........................................................................................................44
4.3.4. Hardware Complexity and Response Time..........................................................47
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4.3.5. Ahead Pipelining...................................................................................................48
4.4. Experimental Results....................................................................................................50
4.4.1. Methodology.........................................................................................................50
4.4.2. Prediction Accuracy..............................................................................................52
4.4.3. Ahead Pipelining...................................................................................................54
4.4.4. Global History PMPM Predictor..........................................................................55
4.5. Summary.......................................................................................................................57
CHAPTER 5. ADDRESS-BRANCH CORRELATION.........................................................58
5.1. Address-Branch Correlation.........................................................................................60
5.1.1. Motivation.............................................................................................................61
5.1.2. Benchmark Study..................................................................................................63
5.1.3. Performance Potential...........................................................................................65
5.2. The Design of an Address-Branch Correlation Based Predictor..................................71
5.2.1. Capturing Load/Branch Pairs with Strong Address-branch Correlation..............72
5.2.2. Tracking Address-Branch Correlation with a Prediction Table...........................75
5.2.3. Linking Producer Loads and Consumer Branches................................................76
5.2.4. Hardware Cost......................................................................................................79
5.3. Simulation Methodology..............................................................................................80
5.4. Experimental Results....................................................................................................81
5.4.1. Performance..........................................................................................................81
5.4.2. Prediction Accuracy and the Reduction in Branch Misprediction Penalties........83
5.4.3. Impact of Primary Branch Predictors...................................................................85
5.4.4. Sensitivity Study...................................................................................................87
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