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A Hybrid Value Predictor using Speculative Update of the Predictor Table and Static Classification for the Pattern of Executed Instructions in Superscalar Processors  

Park, Hong-Jun (극동정보대학 전산정보처리과)
Jo, Young-Il (수원대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.8, no.1, 2002 , pp. 107-115 More about this Journal
Abstract
We propose a new hybrid value predictor which achieves high performance by combining several predictors. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instructions due to stale data. Also, the proposed predictor can enhance the prediction accuracy and efficiently decrease the hardware cost of predictor, because it allocates instructions into the best-suited predictor during instruction fetch stage by using the information of static classification which is obtained from the profile-based compiler implementation. For the 16-issue superscalar processors, simulation results based on the SimpleScalar/PISA tool set show that we achieve the average prediction rates of 73% by using speculative update and the average prediction rates of 88% by adding static classification for the SPECint95 benchmark programs.
Keywords
Instruction-level parallelism; ILP; data value prediction; data value predictor; hybrid value prediction; prediction accuracy;
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Times Cited By KSCI : 1  (Citation Analysis)
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