Piecewise Linear Branch Prediction

Jiménez, Daniel

doi:10.1145/1080695.1070002

Cited by 33 publications

(42 citation statements)

References 26 publications

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“…The Piecewise Linear Predictor 1) Overview: This concept was previously applied in the context of branch prediction [7]. The idea was to exploit the history of a branch in order to predict its outcome (taken or not taken).…”

Section: Dynamic Configuration Prefetchingmentioning

confidence: 99%

“…Between 15% and 40% of the nodes were selected as hardware candidates (those with the highest software execution times), and their hardware execution time was generated β times smaller than their software one. The coefficient β, chosen from the uniform distribution on [3,7], models the variability of hardware speedups. We also generated the size of the candidates, which determined their reconfiguration time.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The software execution times were obtained considering the following cycles per instruction (CPI) values: for calls, returns and floating point instructions CPI = 3, for load, store and branch instructions CPI = 2, and for other instructions CPI = 1. Similar to the previous experimental sections, we considered the hardware execution time β times smaller than the software one, where β was chosen from the uniform distribution on the interval [3,7]. 3 Similar examples are omitted due to space constraints.…”

Section: Case Study -Multimedia Benchmarkmentioning

confidence: 99%

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Dynamic Configuration Prefetching Based on Piecewise Linear Prediction

Lifa

Eles

Peng

2013

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2013

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Abstract-Modern systems demand high performance, as well as high degrees of flexibility and adaptability. Many current applications exhibit a dynamic and nonstationary behavior, having certain characteristics in one phase of their execution, that will change as the applications enter new phases, in a manner unpredictable at design-time. In order to meet the performance requirements of such systems, it is important to have on-line optimization algorithms, coupled with adaptive hardware platforms, that together can adjust to the run-time conditions. We propose an optimization technique that minimizes the expected execution time of an application by dynamically scheduling hardware prefetches. We use a piecewise linear predictor in order to capture correlations and predict the hardware modules to be reached. Experiments show that the proposed algorithm outperforms the previous state-of-art in reducing the expected execution time by up to 27% on average.

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Section: Dynamic Configuration Prefetchingmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Case Study -Multimedia Benchmarkmentioning

confidence: 99%

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Dynamic Configuration Prefetching Based on Piecewise Linear Prediction

Lifa

Eles

Peng

2013

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2013

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“…We also evaluate a perceptron-based predictor, but unlike Jiménez our inputs are based on global and local branch operand difference information. In [11] Jiménez developed a piecewise linear predictor using a piecewise linear function the idea being to exploit different paths leading to the branch undergoing prediction. We have also evaluated a piecewise linear predictor on the unbiased branches as described in [12].…”

Section: Related Workmentioning

confidence: 99%

“…Finally, we considered the impact of unbiased branches on a piecewise linear predictor based on [11]. We dynamically changed the global history input from 18-to 48-bits combined with local history input from 1-to 16-bits.…”

Section: Neural-based Branch Difference Global and Local Predictionmentioning

confidence: 99%

Unbiased Branches: An Open Problem

Gellért

Florea

Vintan

et al.

Advances in Computer Systems Architecture

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Abstract. The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 integer benchmark suite, are interesting even though they show that unbiased branches still restrict the ceiling of branch prediction and therefore accurately predicting unbiased branches remains an open problem.

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A survey of techniques for dynamic branch prediction

Mittal

2018

Concurrency and Computation

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Branch predictor (BP) is an essential component in modern processors since high BP accuracy can improve performance and reduce energy by decreasing the number of instructions executed on wrong-path. However, reducing the latency and storage overhead of BP while maintaining high accuracy presents significant challenges. In this paper, we present a survey of dynamic branch prediction techniques. We classify the works based on key features to underscore their differences and similarities. We believe this paper will spark further research in this area and will be useful for computer architects, processor designers, and researchers. KEYWORDSdynamic branch predictor, hybrid BP, neural BP, perceptron predictorside BP, pipelining, predictor accuracy, review, side BP, speculative execution, two-level BP INTRODUCTIONControl-changing instructions such as branches add uncertainty in the execution of dependent instructions and thus lead to large performance loss in pipelined processors. To offset their overhead, accurate prediction of branch outcomes is vital. Since branch misprediction incurs high latency (eg, 14 to 25 cycles 1 ) and wastes energy due to execution of instructions on wrong-path, an improvement in BP* accuracy can boost performance and energy efficiency significantly. For example, experiments on real processors showed that reducing the branch mispredictions by half improved the processor performance by 13%. 2Effective design and management of BPs, however, presents several challenges. Design of BP involves a strict tradeoff between area, energy, accuracy, and latency. An increase in BP complexity for improving accuracy may make it infeasible for implementation or offset its performance benefit. For example, a 512 KB perceptron predictor may provide lower instruction-per-cycle than its 32 KB version, 3 and a 2-cycle fully accurate BP may provide lower instruction-per-cycle than a 1-cycle partially inaccurate BP. 4 Further, due to its high access frequency, BP becomes a thermal hot-spot, 5 and hence, reducing its dynamic and leakage energy is important. Clearly, intelligent techniques are required for resolving these issues.Further, recently, researchers have demonstrated two security vulnerabilities viz., "Meltdown" and "Spectre," which exploit speculative execution feature of modern CPUs. 6-8 For example, Spectre vulnerability works on the observation that the speculative execution due to a branch misprediction may leave side effects, which can reveal sensitive data to attackers, eg, if the memory accesses performed during speculative execution depends on sensitive data, the resulting state of the cache forms a side-channel through which the sensitive data can be leaked. These vulnerabilities affect nearly every modern CPU. While avoiding speculative execution (eg, disabling branch prediction) can mitigate these vulnerabilities, it leads to significant performance loss. In light of these vulnerabilities and associated tradeoffs, a deeper study of branch predictors is definitely required. Contributions:In this ...

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Piecewise Linear Branch Prediction

Cited by 33 publications

References 26 publications

Dynamic Configuration Prefetching Based on Piecewise Linear Prediction

Dynamic Configuration Prefetching Based on Piecewise Linear Prediction

Unbiased Branches: An Open Problem

A survey of techniques for dynamic branch prediction

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