  With the rise of specialized hardware and new programming languages, code
optimization has shifted its focus towards promoting data locality. Most
production-grade compilers adopt a control-centric mindset - instruction-driven
optimization augmented with scalar-based dataflow - whereas other approaches
provide domain-specific and general purpose data movement minimization, which
can miss important control-flow optimizations. As the two representations are
not commutable, users must choose one over the other. In this paper, we explore
how both control- and data-centric approaches can work in tandem via the
Multi-Level Intermediate Representation (MLIR) framework. Through a combination
of an MLIR dialect and specialized passes, we recover parametric, symbolic
dataflow that can be optimized within the DaCe framework. We combine the two
views into a single pipeline, called DCIR, showing that it is strictly more
powerful than either view. On several benchmarks and a real-world application
in C, we show that our proposed pipeline consistently outperforms MLIR and
automatically uncovers new optimization opportunities with no additional
effort.
