Hybridizing and Coalescing Load Value Predictors

∗ now at Cornell University Abstract Most well-performing load value predictors are hybrids that combine multiple predictors into one. Such hybrids are often large. To reduce their size and to improve their performance, this paper presents two storage reduction techniques as well as a detailed analysis of the interaction between a hybrid’s components. We found that state sharing and simple value compression can shrink the size of a predictor by a factor of two without compromising the performance. Our component analysis revealed that combining well-performing predictors does not always yield a good hybrid, whereas sometimes a poor predictor can make an excellent complement to another predictor in a hybrid. Performance evaluations using a cycle-accurate simulator running SPECint95 show that hybridizing can improve non-hybrids by thirty to fifty percent over a wide range of sizes. With fifteen kilobytes of state, our coalesced-hybrid yields a harmonic mean speedup of twelve and fifteen percent with a re-fetch and a re-execute misprediction recovery mechanism, respectively, which is higher than the speedup of other predictors we evaluate, some of which are six times larger.