Predicting conditional branches with fusion-based hybrid predictors

Loh, Gabriel H.; Henry, Dana S.

doi:10.1109/pact.2002.1106015

Cited by 23 publications

(30 citation statements)

References 19 publications

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“…D. A. Jimenez and C. Lin introduced their research on neural branch prediction in [13] [14] [15]. This branch predictor has a better performance because it can eliminate the history noise of uncorrelative branches.…”

Section: Related Workmentioning

confidence: 99%

Record Branch Prediction: An Optimized Scheme for Two-level Branch Predictors

Chen

Pan

Jiang

et al. 2012

2012 IEEE 14th International Conference on High Performance Computing and Communication &Amp; 2012 IEEE 9th International Confe

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Modern processors get great performance improvement by branch prediction, and two-level branch predictor is widely used because of its simple structure and high performance. But this kind of predictor has 3 limitations-Branch Alias, History Noise and Multi-way Access. In this paper, we propose an optimized mechanism named Record Branch Prediction, which records the PCs and GHR of previous executed branches in order to guide current branch prediction. We apply Record Branch Prediction on the traditional two-level branch predictors and implement Record-Gshare and Record2Bc-gskew. The results show that record predictors can get less prediction miss than original predictor without Record Branch Prediction. Besides, longer PC-Records and larger PHT capacity can reduce more prediction miss percentage for original predictor in general. As an example, Records-Gshare can reduce 7.6% prediction miss compared with Gshare and Records-2Bc-gskew can reduce 14.5% prediction miss compared with 2Bc-gskew on average when number of PC-Records entries is 20 and number of PHT entries is 4096.

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Section: Related Workmentioning

confidence: 99%

Record Branch Prediction: An Optimized Scheme for Two-level Branch Predictors

Chen

Pan

Jiang

et al. 2012

2012 IEEE 14th International Conference on High Performance Computing and Communication &Amp; 2012 IEEE 9th International Confe

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“…The user can configure any number of component direction predictors, and then specify a single meta-predictor that combines the predictions together. The individual components can be any of the supported algorithms (the object-oriented organization of the predictor modules makes adding new algorithms to the simulator very simple), and the meta-predictor can implement a variety of different techniques such as classical tournamentbased selection [17], multi-hybrid selection [5], or fusion techniques [4,12].…”

Section: Fetchmentioning

confidence: 99%

Zesto: A cycle-level simulator for highly detailed microarchitecture exploration

Loh

Subramaniam

Xie

2009

2009 IEEE International Symposium on Performance Analysis of Systems and Software

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For academic computer architecture research, a large number of publicly available simulators make use of relatively simple abstractions for the microarchitecture of the processor pipeline. For some types of studies, such as those for multi-core cache coherence designs, a simple pipeline model may suffice. For detailed microarchitecture research, such as those that are sensitive to the exact behavior of outof-order scheduling, ALU and bypass network contention, and resource management (e.g., RS and ROB entries), an over-simplified model is not representative of modern processor organizations. We present a new timing simulator that models a modern x86 microarchitecture at a very low level, including out-of-order scheduling and execution that much more closely mirrors current implementations, a detailed cache/memory hierarchy, as well as many x86-specific microarchitecture features (e.g., simple vs. complex decoders, micro-op decomposition and fusion, microcode lookup overhead for long/complex x86 instructions).

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“…There are a large number of other candidate algorithms that we could have chosen [6,10,16,20,21,24,26,27,[29][30][31], many of which were not considered because they use non-branch information [32], profile information [5,7], timing information [11], or were similar to other predictors already included in the study [9]. The goal is not to conduct an exhaustive comparison of all predictors ever proposed; branch prediction merely serves as a vehicle to conduct comparisons between simulation infrastructures.…”

Section: Simulated Branch Predictorsmentioning

confidence: 99%

Simulation Differences Between Academia and Industry: A Branch Prediction Case Study

Loh

2005

IEEE International Symposium on Performance Analysis of Systems and Software, 2005. ISPASS 2005.

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Computer architecture research in academia and industry is heavily reliant on simulation studies. While microprocessor companies have the resources to develop highly detailed simulation infrastructures that they correlate against their own silicon, academic researchers tend to use free, widely available simulators. The differences in instruction set architectures, operating systems, simulator models and benchmarks create a disconnect between academic and industrial research studies. This paper presents a comparative study to find correlations and differences between the same microarchitecture study conducted in two different frameworks. Due to the limited availability of industrial simulation frameworks, this research is limited to a case study of branch predictors. Encouragingly, our simulations indicate that several recently proposed branch predictors behave similarly in both environments when evaluated with the SPECcpu benchmark suite. Unfortunately, we also present results that show that conclusions drawn from studies based on SPECcpu do not necessarily hold when other applications are considered.

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Predicting conditional branches with fusion-based hybrid predictors

Cited by 23 publications

References 19 publications

Record Branch Prediction: An Optimized Scheme for Two-level Branch Predictors

Record Branch Prediction: An Optimized Scheme for Two-level Branch Predictors

Zesto: A cycle-level simulator for highly detailed microarchitecture exploration

Simulation Differences Between Academia and Industry: A Branch Prediction Case Study

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