Abstract
Data value prediction has been widely accepted as an effective mechanism to exceed the dataflow limit in processor parallelism race. Several works have reported promising performance potential. However, there is hardly enough information that is presented in a clear way about performance comparison of these prediction mechanisms. This paper investigates the performance impact of four previously proposed value predictors, namely last value predictor, stride value predictor, two-level value predictor and hybrid (stride+two-level) predictor. The impact of misprediction penalty, which has been frequently ignored, is discussed in detail. Several other implementation issues, including instruction window size, issue width and branch predictor are also addressed and simulated. Simulation results indicate that data value predictors act differently under different configurations. In some cases, simpler schemes may be more beneficial than complicated ones. In some particular cases, value prediction may have negative impact on performance.
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Xiao, Y., Deng, K., Zhou, X. (2004). Performance Impact of Different Data Value Predictors. In: Yew, PC., Xue, J. (eds) Advances in Computer Systems Architecture. ACSAC 2004. Lecture Notes in Computer Science, vol 3189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30102-8_35
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DOI: https://doi.org/10.1007/978-3-540-30102-8_35
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