Existing research work on data speculative optimizations
has mainly focused on individual code transformation. The
required speculative analysis that identifies data
speculative optimization opportunities and the required
recovery code generation that guarantees the correctness of
their execution are handled separately for each
optimization. This talk proposes a new compiler framework
to exploit general data speculative optimizations such as
dead store elimination, redundancy elimination, copy
propagation, and code scheduling. In this framework, a
shared speculative analysis which hides low probability
data dependences from optimizations is provided to help
them identify speculative optimization opportunities. In
addition, a shared recovery code generation mechanism is
provided to guarantee the correctness of the data
speculative optimizations. The proposed recovery code
generation is based on Data Speculative Code Motion (DSCM)
that uses code motion to facilitate a desired
transformation. The proposed framework greatly simplifies
the task of incorporating data speculation into
non-speculative optimizations by sharing the recovery code
generation and the speculative analysis.