Leveraging Strength-Based Dynamic Information Flow Analysis to Enhance Data Value Prediction
ACM Transactions on Architecture and Code Optimization (TACO)
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Predicting the values that are likely to be produced by instructions has been suggested as a way of increasing the instruction-level parallelism available in a superscalar processor. One of the potential difficulties in cost-effectively predicting values for a given instruction, however, is selecting the proper type of predictor, such as a last-value predictor, a stride predictor, or a context-based predictor. We propose a compiler-directed classification scheme that statically partitions all of the instructions in a program into several groups, each of which is associated with a specific value predictability pattern. This value predictability pattern is encoded into the instructions to identify the type of value predictor that will be best suited for predicting the values that are likely to be produced by each instruction at runtime. Both a profile-based compiler implementation and an implementation based on the GCC compiler are studied to show the performance bounds for the proposed technique. Our simulations using an extension to the SimpleScalar tool set and the SPEC95 and SPEC2000 benchmark programs indicate that this approach can efficiently use the limited hardware resources in superscalar processors. This static partitioning approach produces better performance than a dynamically partitioned approach and a simple round-robin distribution approach for a given hardware configuration. Finally, we demonstrate the connection between value locality behavior and source-level program structures, thereby leading to a deeper understanding of the causes of this behavior.