NDLI: Highly accurate data value prediction using hybrid predictors

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Highly accurate data value prediction using hybrid predictors

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Highly accurate data value prediction using hybrid predictors

Content Provider	ACM Digital Library
Author	Wang, Kai Franklin, Manoj
Abstract	Data dependences (data flow constraints) present a major hurdle to the amount of instruction-level parallelism that can be exploited from a program. Recent work has suggested that the limits imposed by data dependences can be overcome to some extent with the use of data value prediction. That is, when an instruction is fetched, its result can be predicted so that subsequent instructions that depend on the result can use this predicted value. When the correct result becomes available, all instructions that are data dependent on that prediction can be validated. This paper investigates a variety of techniques to carry out highly accurate data value predictions. The first technique investigates the potential of monitoring the strides by which the results produced by different instances of an instruction change. The second technique investigates the potential of pattern-based two-level prediction schemes. Simulation results of these two schemes show improvements over the existing method of predicting the last outcome. In particular, some benchmarks show improvement with the stride-based predictor and others show improvement with the pattern-based predictor. To do uniformly well across benchmarks, we combine these two predictors to form a hybrid predictor. Simulation analysis of the hybrid predictor shows its overall prediction accuracy to be better than that of the component predictors across all benchmarks.
Starting Page	281
Ending Page	290
Page Count	10
File Format	PDF
ISBN	0818679778
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1997-12-01
Access Restriction	Subscribed
Subject Keyword	Data speculation instruction-level parallel (ilp) processing speculative execution stride-based prediction two-level prediction
Content Type	Text
Resource Type	Article

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