The Role of Return Value Prediction in Exploiting Speculative Method-Level Parallelism

This work studies the performance impact of return value prediction in a system that supports speculative method-level parallelism (SMLP). A SMLP system creates a speculative thread at each method call. This allows the method and the code from which it is called to be executed in parallel. To improve performance, the return values of methods are predicted in hardware so that no method has to wait for its sub-method to complete before continuing to execute. We find that two-thirds of return values need to be predicted, and perfect return value prediction improves performance by an average of 44% over no return value prediction. However, the performance of realistic predictors is limited by poor prediction accuracy on integer return values and unfavorable SMLP conditions. A new Parameter Stride (PS) predictor is proposed to overcome the deficiencies of the standard predictors by predicting based on method arguments. Combining the PS predictor with previous predictors results in an average 7% speedup versus a system with hybrid return value prediction and 21% speedup versus no return value prediction.