Branch Prediction Is Not A Solved Problem: Measurements, Opportunities, and Future Directions

Lin, Chit-Kwan; Tarsa, Stephen J.

doi:10.1109/iiswc47752.2019.9042108

Cited by 10 publications

(10 citation statements)

References 18 publications

(19 reference statements)

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“…This storage efficiency issue is in line with previous work that exposed it by examining TAGE-SC-L 64KB with large code-footprint applications [22]. Storing only sparse correlations could therefore generally improve the performance of state-of-the-art branch predictors by eliminating the need to represent irrelevant features in their storage, thereby reducing the predictor's footprint.…”

Section: Sparse Branch Correlationssupporting

confidence: 76%

“…4. Our proposal assumes a deployment scenario where predictions are generated at runtime after an offline training phase, as also considered in the work of Lin & Tarsa [22]. Offline training is necessary to capture the predictive statistics of the otherwise hardly detectable sparse correlations.…”

Section: Sparse Predictor Architecturementioning

confidence: 99%

“…Over the past 15 years, branch prediction has been primarily focused on these two designs, resulting in some highly sophisticated branch predictors that can overall achieve remarkably high prediction accuracy [18,28]. However, recent studies demonstrate that the missing accuracy gap limits substantially the performance scalability of future processors [4,22]. As the further enhancement of BP methods is becoming increasingly challenging, performance improvements have stagnated the recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, in the era where data center/cloud applications surge, the otherwise overwhelming cost of offline training can be amortized through economies of scale. In this work, we employ an "offline-training/online-inference" paradigm, as detailed by Lin & Tarsa [22], for introducing sparsity-aware branch prediction. Specifically, our contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

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Identifying and Exploiting Sparse Branch Correlations for Optimizing Branch Prediction

Zouzias¹,

Kalaitzidis²,

Berestizshevsky³

et al. 2022

Preprint

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Branch prediction is arguably one of the most important speculative mechanisms within a high-performance processor architecture. A common approach to improve branch prediction accuracy is to employ lengthy history records of previously seen branch directions to capture distant correlations between branches. The larger the history, the richer the information that the predictor can exploit for discovering predictive patterns. However, without appropriate filtering, such an approach may also heavily disorganize the predictor's internal mechanisms, leading to diminishing returns. This paper studies a fundamental control-flow property: the sparsity in the correlation between branches and recent history. First, we show that sparse branch correlations exist in standard applications and, more importantly, such correlations can be computed efficiently using sparse modeling methods. Second, we introduce a sparsity-aware branch prediction mechanism that can compactly encode and store sparse models to unlock essential performance opportunities. We evaluated our approach for various design parameters demonstrating MPKI improvements of up to 42% (2.3% on average) with 2KB of additional storage overhead. Our circuit-level evaluation of the design showed that it can operate within accepted branch prediction latencies, and under reasonable power and area limitations.

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Section: Sparse Branch Correlationssupporting

confidence: 76%

Section: Sparse Predictor Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identifying and Exploiting Sparse Branch Correlations for Optimizing Branch Prediction

Zouzias¹,

Kalaitzidis²,

Berestizshevsky³

et al. 2022

Preprint

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“…Recent work [12] shows that even though the current stateof-the-art branch predictors have almost perfect prediction accuracy, there is scope for gaining significant performance by fixing the remaining mispredictions. The core architecture could be tuned to be wider if it had the support of better branch prediction, which could potentially offer more IPC gains.…”

Section: Introductionmentioning

confidence: 99%

Load Driven Branch Predictor (LDBP)

Sridhar,

Kabylkas,

Renau

2020

Preprint

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Branch instructions dependent on hard-to-predict load data are the leading branch misprediction contributors. Current state-of-the-art history-based branch predictors have poor prediction accuracy for these branches. Prior research backs this observation by showing that increasing the size of a 256-KBit history-based branch predictor to its 1-MBit variant has just a 10% reduction in branch mispredictions.We present the novel Load Driven Branch Predictor (LDBP) specifically targeting hard-to-predict branches dependent on a load instruction. Though random load data determines the outcome for these branches, the load address for most of these data has a predictable pattern. This is an observable template in data structures like arrays and maps. Our predictor model exploits this behavior to trigger future loads associated with branches ahead of time and use its data to predict the branch's outcome. The predictable loads are tracked, and the precomputed outcomes of the branch instruction are buffered for making predictions. Our experimental results show that compared to a standalone 256-Kbit IMLI predictor, when LDBP is augmented with a 150-Kbit IMLI, it reduces the average branch mispredictions by 20% and improves average IPC by 13.1% for benchmarks from SPEC CINT2006 and GAP benchmark suite.

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