  Domain Specific Languages (DSLs) increase programmer productivity and provide
high performance. Their targeted abstractions allow scientists to express
problems at a high level, providing rich details that optimizing compilers can
exploit to target current- and next-generation supercomputers. The convenience
and performance of DSLs come with significant development and maintenance
costs. The siloed design of DSL compilers and the resulting inability to
benefit from shared infrastructure cause uncertainties around longevity and the
adoption of DSLs at scale. By tailoring the broadly-adopted MLIR compiler
framework to HPC, we bring the same synergies that the machine learning
community already exploits across their DSLs (e.g. Tensorflow, PyTorch) to the
finite-difference stencil HPC community. We introduce new HPC-specific
abstractions for message passing targeting distributed stencil computations. We
demonstrate the sharing of common components across three distinct HPC
stencil-DSL compilers: Devito, PSyclone, and the Open Earth Compiler, showing
that our framework generates high-performance executables based upon a shared
compiler ecosystem.
