Lowering: A Static Optimization Technique for Transparent Functional Reactivity, Kimberley Burchett, Gregory H. Cooper, and Shriram Krishnamurthi. PEPM 2007.

Functional Reactive Programming (FRP) extends traditional functional programming with dataflow evaluation, making it possible to write interactive programs in a declarative style. An FRP language creates a dynamic graph of data dependencies and reacts to changes by propagating updates through the graph. In a transparent FRP language, the primitive operators are implicitly lifted, so they construct graph nodes when they are applied to time-varying values. This model has some attractive properties, but it tends to produce a large graph that is costly to maintain. In this paper, we develop a transformation we call lowering, which improves performance by reducing the size of the graph. We present a static analysis that guides the sound application of this optimization, and we present benchmark results that demonstrate dramatic improvements in both speed and memory usage for real programs.

Whenever I read about compiler optimizations, I try (with mixed success) to relate them to transformations in the lambda calculus. I haven't managed to figure out what's going on with the `dip`

construct the authors propose, but I would guess that the place to look is the proof theory of the necessity operator in modal logic -- dataflow programming can be seen as a kind of stream programming, and streams form a comonad over the lambda calculus, and comonads give semantics to the modal necessity (box) operator.

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