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A Hybrid Value Predictor using Static and Dynamic Classification in Superscalar Processors  

김주익 (수원대학교 컴퓨터학과)
박홍준 (극동정보대학 전산정보처리과)
조영일 (수원대학교 정보공학대학 컴퓨터학과)
Publication Information
Journal of KIISE:Computer Systems and Theory / v.30, no.10, 2003 , pp. 569-578 More about this Journal
Abstract
Data dependencies are one of major hurdles to limit ILP(Instruction Level Parallelism), so several related works have suggested that the limit imposed by data dependencies can be overcome to some extent with use of the data value prediction. Hybrid value predictor can obtain the high prediction accuracy using advantages of various predictors, but it has a defect that same instruction has overlapping entries in all predictor. In this paper, we propose a new hybrid value predictor which achieves high performance by using the information of static and dynamic classification. The proposed predictor can enhance the prediction accuracy and efficiently decrease the prediction table size of predictor, because it allocates each instruction into single best-suited predictor during the fetch stage by using the information of static classification. Also, it can enhance the prediction accuracy because it selects a best- suited prediction method for the “Unknown”pattern instructions by using the dynamic classification mechanism. Simulation results based on the SimpleScalar/PISA tool set and the SPECint95 benchmarks show the average correct prediction rate of 85.1% by using the static classification mechanism. Also, we achieve the average correction prediction rate of 87.6% by using static and dynamic classification mechanism.
Keywords
Superscalar Processor; Data Dependency; Instruction-Level Parallelism; Dynamic Classification; Static Classification; Hybrid Value Prediction;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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