The symbolic treatment of mathematical models is essential to study the dynamics of complex physical systems by means of numerical simulations. In contrast to signal flow based approaches, modeling with equation-based and object-oriented languages has the advantage that the original equations can be used directly. The acausal description of equations increases reusability and reduces the effort for the modeller. The automated transformation of equations into assignments can in theory be applied to models of any size and complexity. In practice, however, problems arise when large models, i.e. mathematical systems with many equations and time-dependent variables, shall be transformed. Long execution times that occur limit the applicability of symbolic processing considerably. The present work describes the process of automated transformation from a model to program code which can be simulated numerically. All steps are examined in detail and evaluated in terms of its theoretical complexity. Suitable algorithms are implemented in the OpenModelica Compiler. Their execution times are compared by looking at models which are relevant to engineering. The best implementations for each sub-step are selected and combined to a Modelica Compiler BackEnd. Thus a relationship between model size and the time needed for transformation has been achieved which is mostly linear. In addition, a comprehensive discussion with numerous recommendations for the implementation of a Modelica Compiler BackEnd is given. This is supposed to help new developers as well as to facilitate the development of existing algorithms. The latter is supported by a modular concept of a BackEnd pipeline. Moreover, methods are discussed how new modules can be tested efficiently using this pipeline.