Lowering: A Static Optimization Technique for Transparent Functional Reactivity
Kimberley Burchett, Gregory H. Cooper, Shriram Krishnamurthi
ACM SIGPLAN Symposium on Partial Evaluation and Semantics-Based Program Manipulation, 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.
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