The recently introduced _Perceus_ algorithm can automatically insert reference count instructions such that the resulting (cycle-free) program is _garbage free_: objects are freed at the very moment they can no longer be referenced. An important extension is reuse analysis. This optimization pairs objects of known size with fresh allocations of the same size and tries to reuse the object in-place at runtime if it happens to be unique. Unfortunately, current implementations of reuse analysis are fragile with respect to small program transformations, or can cause an arbitrary increase in the peak heap usage. We present a novel _drop-guided_ reuse algorithm that is simpler and more robust than previous approaches. Moreover, we generalize the linear resource calculus to precisely characterize garbage-free and frame-limited evaluations. On each function call, a frame-limited evaluation may hold on to memory longer if the size is bounded by a constant factor. Using this framework we show that our drop-guided reuse _is_ frame-limited and find that an implementation of our new reuse approach in Koka can provide significant speedups.<\/p>\n","protected":false},"excerpt":{"rendered":"
The recently introduced _Perceus_ algorithm can automatically insert reference count instructions such that the resulting (cycle-free) program is _garbage free_: objects are freed at the very moment they can no longer be referenced. An important extension is reuse analysis. This optimization pairs objects of known size with fresh allocations of the same size and tries 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