An Efficient Value Predictor Dynamically Using Loop and Locality Properties

Abstract

Value prediction, a technique to break data dependency, is important in enhancing instruction-level parallelism and processor performance. A new value predictor utilizing both the loop and locality properties of data values has been proposed in this paper to pursue desirable prediction accuracy at reasonable cost. The proposed value predictor, called the Dynamic Loop and Locality-based (DLL) predictor, makes predictions by dynamically practicing the loop or locality-based prediction policy according to the state. With certain simple designs, the DLL predictor gains prediction accuracy in an efficient way. To secure more comprehensive experimental evaluation of value predictors, a new performance measure, accuracy improvement per cost, briefed as the A/C ratio, is introduced in the paper. Simulation results show that, compared with other existing value predictors, the proposed DLL predictor produces better A/C ratios in almost all situations due to flexible application of different prediction policies and reduced cost.