Control-flow speculation through value prediction for superscalar processors

In this paper, we introduce a new branch predictor that predicts the outcomes of branches by predicting the value of their inputs and performing an early computation of their results according to the predicted values. The design of a hybrid predictor comprising our branch predictor and a correlating branch predictor is presented. We also propose a new selector that chooses the most reliable prediction for each branch. This selector is based on the path followed to reach the branch. Results for immediate updates show a significant improvement with respect to a conventional hybrid predictor for different size configurations. In addition, the proposed hybrid predictor with a size of 8 KB achieves the same miss ratio as a conventional one of 64 KB. Performance evaluation for a dynamically-scheduled superscalar processor, with realistic updates, shows a speed-up of 11% despite its higher latency (up to 4 cycles)Peer ReviewedPostprint (published version

Improving virtual function call target prediction via dependence-based pre-computation

We introduce dependence-based pre-computation as a complement to history-based target prediction schemes. We present pre-computation in the context of virtual function calls (v-calls), a class of control transfers that is becoming increasingly important and has resisted conventional prediction. Our proposed technique dynamically identifies the sequence of operations that computes a v-call’s target. When the first instruction in such a sequence is encountered, a small execution engine speculatively and aggressively pre-executes the rest. The pre-computed target is stored and subsequently used when a prediction needs to be made. We show that a common v-call instruction sequence can be exploited to implement pre-computation using a previously proposed prefetching mechanism and minimal additional hardware. In a suite of C++ programs, dependence-based pre-computation eliminates 46 % of the mispredictions incurred by a simple BTB and 24 % of those associated with a path-based two-level predictor.