Data-race-free (DRF) parallel programming becomes a standard as newly adopted memory models of mainstream programming languages such as C++ or Java impose data-racefreedom as a requirement.We propose compiler techniques that automatically delineate extended data-race-free regions (xDRF), namely regions of code which provide the same guarantees as the synchronization-free regions (in the context of DRF codes). xDRF regions stretch across synchronization boundaries, function calls and loop back-edges and preserve the data-racefree semantics, thus increasing the optimization opportunities exposed to the compiler and to the underlying architecture. Our compiler techniques precisely analyze the threads' memory accessing behavior and data sharing in shared-memory, general-purpose parallel applications and can therefore infer the limits of xDRF code regions.We evaluate the potential of our technique by employing the xDRF region classification in a state-of-the-art, dualmode cache coherence protocol. Larger xDRF regions reduce the coherence bookkeeping and enable optimizations for performance (6.8%) and energy efficiency (11.7%) compared to a standard directory-based coherence protocol.

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