Static Java Program Features for Intelligent Squash Prediction

The thread-level speculation paradigm parallelizes sequential applications at run-time, via optimistic execution of potentially independent threads. This enables unmodified sequential applications to exploit thread-level parallelism on modern multicore architectures. However a high frequency of data dependence violations between speculative threads can severely degrade the performance of thread-level speculation. Thus it is crucial to be able to schedule speculations to avoid excessive data dependence violations. Previous work in this area relies mainly on program profiling or simple heuristics to avoid thread squashes. In this paper, we investigate the use of machine learning to construct squash predictors based on static program features. On a set of standard Java benchmarks, with leave-one-out cross-validation, our approach significantly improves speculation performance for two benchmarks, but unfortunately degrades it for another two, in relation to a spawn-everywhere policy. We discuss how to advance research on squash prediction, directed by machine learning.