cient Incremental Run Time Specialization for Free Renaud

Availability of data in a program determines compu tation stages Incremental partial evaluation exploit these stages for optimization it allows further special ization to be performed as data become available at later stages The fundamental advantage of incremental specialization is to factorize the specialization process As a result specializing a program at a given stage costs considerably less than specializing it once all the data are available We present a realistic and exible approach to achieve e cient incremental run time specialization Rather than developing speci c techniques as previous ly proposed we are able to re use existing technology by iterating a specialization process Moreover in do ing so we do not lose any specialization opportunities This approach makes it possible to exploit nested quasi invariants and to speed up the run time specialization process Our approach has been implemented in Tempo a specializer for C programs that is publicly available A preliminary experiment con rms that incremental spe cialization can greatly speed up the specialization pro cess