A new method to prevent control hazard in pipeline processor by using an auxiliary processing unit

Navin, Ahmad Habibizad; Lahouti, Ehsan; Anhar, Mahmoud Lotfi; Mirnia, Mir Kamal

doi:10.1109/icacc.2010.5487130

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…The more common pipeline hazards are data hazards and control hazards. Much literature has provided solutions for these two hazards [12,13,14,15,16] . For example, pipeline pauses and bubbles can be used to deal with data hazards in the pipeline.…”

Section: Introductionmentioning

confidence: 99%

Minimizing control dependencies of pipelining through optimizing branch selection

Gao,

Shao

2024

Preprint

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In modern computer systems, processors achieve high performance through pipelining. Therefore, the execution efficiency of the pipelining is one of the critical factors impacting processor performance. The main factors that impact pipelining efficiency are data hazards caused by data dependencies and control hazards caused by control dependencies. Regarding the control hazards present in pipelining, although a few technologies are mentioned in the literature for addressing them, they frequently require significantly high latency and may not guarantee optimal quality. This paper introduces the Functional Function (FF), derived from the Fork function; by selectively executing branch instructions based on previous calculation results, the Functional Function can minimize control dependencies in pipelining and enhance the utilization of the processor pipelining via optimizing branch selection. The Intel VTune Profiler program analyzer was employed for data collection and analysis during the experiment to facilitate an unbiased comparison with existing methods. The experimental results indicate that the Functional Function significantly outperforms the existing methods, primarily reflected in the Pipeline utilization rate, CPI rate, and Bad Speculation rate. Furthermore, the utilization of the pipelining is enhanced by approximately 20%, accompanied by a nearly 1.5% increase in branch prediction accuracy compared with existing methods.

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Section: Introductionmentioning

confidence: 99%