  Intermediate Representations (IRs) are central to optimizing compilers as the
way the program is represented may enhance or limit analyses and
transformations. Suitable IRs focus on exposing the most relevant information
and establish invariants that different compiler passes can rely on. While
control-flow centric IRs appear to be a natural fit for imperative programming
languages, analyses required by compilers have increasingly shifted to
understand data dependencies and work at multiple abstraction layers at the
same time. This is partially evidenced in recent developments such as the MLIR
proposed by Google. However, rigorous use of data flow centric IRs in general
purpose compilers has not been evaluated for feasibility and usability as
previous works provide no practical implementations. We present the
Regionalized Value State Dependence Graph (RVSDG) IR for optimizing compilers.
The RVSDG is a data flow centric IR where nodes represent computations, edges
represent computational dependencies, and regions capture the hierarchical
structure of programs. It represents programs in demand-dependence form,
implicitly supports structured control flow, and models entire programs within
a single IR. We provide a complete specification of the RVSDG, construction and
destruction methods, as well as exemplify its utility by presenting Dead Node
and Common Node Elimination optimizations. We implemented a prototype compiler
and evaluate it in terms of performance, code size, compilation time, and
representational overhead. Our results indicate that the RVSDG can serve as a
competitive IR in optimizing compilers while reducing complexity.
