In-Place Update in a Dataflow Synchronous Language: A Retiming-Enabled Language Experiment

Abstract

Dataflow synchronous languages such as Lustre have a purely functional semantics. This incurs a high overhead when dealing with arrays, as they have to be copied at each update. We propose to tackle this problem at the source, by constraining programs so that every functional array definition can be optimized into an in-place update. Our solution handles aliasing between function arguments. It also allows more programs with in-place updates to be accepted thanks to a new retiming framework, effectively rescheduling computations across time steps. Our proposed language and compilation method enforces zero-copy purely functional arrays while preserving expressiveness and programmer control through explicit copies.