  Data race free (DRF) programs constitute an important class of concurrent
programs. In this paper we provide a framework for designing and proving the
correctness of data flow analyses that target this class of programs. These
analyses are in the same spirit as the "sync-CFG" analysis proposed in earlier
literature. To achieve this, we first propose a novel concrete semantics for
DRF programs, called L-DRF, that is thread-local in nature---each thread
operates on its own copy of the data state. We show that abstractions of our
semantics allow us to reduce the analysis of DRF programs to a sequential
analysis. This aids in rapidly porting existing sequential analyses to sound
and scalable analyses for DRF programs. Next, we parameterize L-DRF with a
partitioning of the program variables into "regions" which are accessed
atomically. Abstractions of the region-parameterized semantics yield more
precise analyses for "region-race" free concurrent programs. We instantiate
these abstractions to devise efficient relational analyses for race free
programs, which we have implemented in a prototype tool called RATCOP. On the
benchmarks, RATCOP was able to prove up to 65% of the assertions, in comparison
to 25% proved by our baseline. Moreover, in a comparative study with a recent
concurrent static analyzer, RATCOP was up to 5 orders of magnitude faster.
