Analysis of pure methods using garbage collection

Abstract

Parallelization and other optimizations often depend on static dependence analysis. This approach requires methods to be independent regardless of the input data, which is not always the case. Our contribution is a dynamic analysis "guessing" if methods are pure, i. e., if they do not change state. The analysis is piggybacking on a garbage collector, more specifically, a concurrent, replicating garbage collector. It guesses whether objects are immutable by looking at actual mutations observed by the garbage collector. The analysis is essentially for free. In fact, our concurrent garbage collector including analysis outperforms Boehm's stop-the-world collector (without any analysis), as we show in experiments. Moreover, false guesses can be rolled back efficiently. The results can be used for just-in-time parallelization allowing an automatic parallelization of methods that are pure over certain periods of time. Hence, compared to parallelization based on static dependence analysis, more programs potentially benefit from parallelization.