Efficient and accurate data dependence profiling using software signatures

Abstract

Speculative optimizations relax conservative constraints, like ambiguous memory-carried dependences that will rarely occur at runtime, to allow compilers to generate higher performing code. Data dependence profiling enables these techniques by providing runtime dependence information. However, prior data dependence profilers (DDPs) tend to be slow or have significant inaccuracy. Such techniques try to track all pairs of dependences that occur at runtime. To have high accuracy, it usually requires significant storage and performance overhead. Of course, accuracy can be sacrificed via sampling or fixed size storage to provide higher performance; but, these knobs are tough to control and often lead to a significant loss in accuracy. Hence, we search for a better trade-off between speed and accuracy, and we achieve this by considering a new approach based on efficient set operations using software signatures.