The impact of unresolved branches on branch prediction scheme performance

1994 ◽  
Vol 22 (2) ◽  
pp. 12-21 ◽  
Author(s):  
A. R. Talcott ◽  
W. Yamamoto ◽  
M. J. Serrano ◽  
R. C. Wood ◽  
M. Nemirovsky
Keyword(s):  
Branch Prediction ◽  
Prediction Scheme ◽  
The Impact

scholarly journals Pronounced Impact of Salinity on Rapidly Intensifying Tropical Cyclones

2020 ◽  
Vol 101 (9) ◽  
pp. E1497-E1511 ◽  
Author(s):  
Karthik Balaguru ◽  
Gregory R. Foltz ◽  
L. Ruby Leung ◽  
John Kaplan ◽  
Wenwei Xu ◽  
...  
Keyword(s):  
Positive Impact ◽  
River System ◽  
Surface Salinity ◽  
Near Surface ◽  
Salinity Stratification ◽  
Sst Cooling ◽  
Prediction Scheme ◽  
Ocean Salinity ◽  
Global Coverage ◽  
The Impact

Abstract Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.

Fault Prediction of Pitch Actuator for Wind Turbines

2014 ◽  
Vol 721 ◽  
pp. 397-401
Author(s):  
Hong Shan Zhao ◽  
Sha Sha Lian ◽  
Ling Shao
Keyword(s):  
Wind Turbines ◽  
Prediction Method ◽  
Fault Isolation ◽  
Fault Prediction ◽  
False Alarms ◽  
Controlled System ◽  
Pitch Control ◽  
Prediction Scheme ◽  
The Impact ◽  
The Mathematical Model

Hydraulic pitch-controlled system is one of the components of wind turbines which are frequently prone to faults. Early fault prediction of the pitch control system can improve the operation reliability effectively and reduce the unnecessary loss. Wind turbines suffer much environmental interference; moreover, data-based fault prediction is vulnerable to occur false alarms by the impact of these factors. And it is difficult to implement the fault isolation. So this paper presents a fault prediction method for the pitch-controlled system, which is based on the mathematical model of wind turbines physical properties. The residual root mean square (RMS) is used as residual evaluation function. In the end of the paper, by the simulation using the hydraulic pitch actuator fault as the example, the effectiveness of the proposed fault prediction scheme is verified.

Characterizing the impact of predicated execution on branch prediction

1994 ◽  
Author(s):  
Scott A. Mahlke ◽  
Richard E. Hank ◽  
Roger A. Bringmann ◽  
John C. Gyllenhaal ◽  
David M. Gallagher ◽  
...  
Keyword(s):  
Branch Prediction ◽  
Predicated Execution ◽  
The Impact

A Hybrid Branch Prediction Approach for High- Performance Processors

2021 ◽  
Vol 14 ◽  
Author(s):  
Sweety Nain ◽  
Prachi Chaudhary
Keyword(s):  
High Performance ◽  
Clock Cycle ◽  
Branch Prediction ◽  
Accuracy Rate ◽  
Local Branch ◽  
Prediction Scheme ◽  
Prediction Technique ◽  
Global And Local ◽  
Prediction Approach ◽  
Single Branch

Background: In a parallel processor, the pipeline cannot fetch the conditional instructions with the next clock cycle, leading to a pipeline stall. So, conditional instructions create a problem in the pipeline because the proper path can only be known after the branch execution. To accurately predict branches, a significant predictor is proposed for the prediction of conditional branch instruction. Method: In this paper, a single branch prediction and a correlation branch prediction scheme are applied to the different trace files by using the concept of saturating counters. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. Results: Firstly, a single branch prediction and correlation branch prediction technique are applied to the trace files using saturating counters. By comparison, it can be observed that a correlation branch prediction technique provides better results by enhancing the accuracy rate of 2.25% than the simple branch prediction. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. The obtained results suggest that the proposed hybrid branch prediction schemes provide an increased accuracy rate of 3.68% and 1.43% than single branch prediction and correlation branch prediction. Conclusion: The proposed hybrid branch prediction scheme gives a lower misprediction rate and higher accuracy rate than the simple branch prediction scheme and correlation branch prediction scheme.

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