  Equation-based modelling is a powerful approach to tame the complexity of
large-scale simulation problems. Equation-based tools automatically translate
models into imperative languages. When confronted with nowadays' problems,
however, well assessed model translation techniques exhibit scalability issues,
that are particularly severe when models contain very large arrays. In fact,
such models can be made very compact by enclosing equations into looping
constructs, but reflecting the same compactness into the translated imperative
code is not trivial. In this paper, we face this issue by concentrating on a
key step of equations-to-code translation, the equation/variable matching. We
first show that an efficient translation of models with (large) arrays needs
awareness of their presence, by defining a figure of merit to measure how much
the looping constructs are preserved along the translation. We then show that
the said figure of merit allows to define an optimal array-aware matching, and
as our main result, that the so stated optimal array-aware matching problem is
NP-complete. As an additional result, we propose a heuristic algorithm capable
of performing array-aware matching in polynomial time. The proposed algorithm
can be proficiently used by model translator developers in the implementation
of efficient tools for large-scale system simulation.
