Focusing processor policies via critical-path prediction

Although some instructions hurt performance more than others, current processors typically apply scheduling and speculation as if each instruction was equally costly. Instruction cost can be naturally expressed through the critical path: if we could predict it at run-time, egalitarian policies could be replaced with cost-sensitive strategies that will grow increasingly effective as processors become more parallel. This paper introduces a hardware predictor of instruction criticality and uses it to improve performance. The predictor is both effective and simple in its hardware implementation. The effectiveness at improving performance stems from using a dependence-graph model of the microarchitectural critical path that identifies execution bottlenecks by incorporating both data and machine-specific dependences. The simplicity stems from a token-passing algorithm that computes the critical path without actually building the dependence graph. By focusing processor policies on critical instructions, our predictor enables a large class of optimizations. It can (i) give priority to critical instructions for scarce resources (functional units, ports, predictor entries); and (ii) suppress speculation on non-critical instructions, thus reducing “useless” misspeculations. We present two case studies that illustrate the potential of the two types of optimization, we show that (i) critical-path-based dynamic instruction scheduling and steering in a clustered architecture improves performance by as much as 21% (10% on average); and (ii) focusing value prediction only on critical instructions improves performance by as much as 5%, due to removing nearly half of the misspeculations.