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Section: New Results

Environments and the Complexity of Abstract Machines

Participant : Beniamino Accattoli.

This joint work with Bruno Barras (Inria) [30] belongs to line of work Cost Models and Abstract Machines for Functional Languages, supported by the ANR project COCA HOLA.

We study various notions of environments (local, global, split) for abstract machines for functional languages, from a complexity and implementative point of view.

An environment is a data structure used to implement sharing of subterms. There are two main styles. The most common one is to have many local environments, one for every piece of code in the data structures of the machine. A minority of works uses a single global environment instead. Up to now, the two approaches have been considered equivalent, in particular at the level of the complexity of the overhead: they have both been used to obtain bilinear bounds, that is, linear in the number of beta steps and in the size of the initial term.

Our main result is that local environments admit implementations that are asymptotically faster than global environments, lowering the dependency from the size of the initial term from linear to logarithmic, thus improving the bounds in the literature. We also show that a third style, split environments, that are in between local and global ones, has the benefits of both. Finally, we provide a call-by-need machine with split environments for which we prove the new improved bounds on the overhead.