Space optimization in the FNC-2 attribute grammar system
Résumé
Memory space management for attribute evaluators is a vital requirement in practice. In fact, using attribute grammars (AGs) will very quickly meet the problem of memory space if it isn't taken into special consideration. We consider this problem for evaluators of the simple multi-visit class, also called l-ordered, because it is the largest possible AGs class for which we can find, at construction time, a method for memory space optimization. We present a new algorithm which decides, at generation time, if it is possible to store attribute instances in global stacks or global variables. The purpose of this approach is to reduce not only memory space, but also as much as possible the number of attributes to be stored in the nodes of the tree. This method is implemented in the new attribute grammar processing system, named FNC-2. Finally we present our first practical results which seem very promising.