Algebraic data types and pattern matching are popular tools to build programs manipulating complex datastructures in a safe yet efficient manner. On top of its safety advantages, compilation techniques can turn pattern matching into highly efficient deconstruction code for immutable use cases.Conversely, high-performance datastructures and languages prefer to leverage (controlled) mutations to maximize time and memory efficiency. Algebraic data types provide a natural framework to efficiently describe in-place transformations as rewrite rules. Such representation could take advantage of parallelism opportunities that appear in tree-like structures.We present early steps towards a new technique to compile pattern matching as parallel in-place modifications of the underlying memory representation. Towards this goal, we combine the usual language approach which is common in pattern-matching compilation with tools from the polyhedral model, which is commonly used in high-performance code generation to output efficient C code. We present our formalism, along with a prototype implementation.