As functional programmers we always face a dilemma: should we write purely\u00a0functional code, or sacrifice purity for efficiency and resort to in-place\u00a0updates? This paper identifies precisely when we can have the best of both\u00a0worlds: a wide class of purely functional programs can be executed safely using in-place updates without requiring allocation, provided their arguments are not\u00a0shared elsewhere. We describe a linear _fully in-place_ (FIP) calculus where we prove that we can\u00a0always execute such functions in a way that requires no (de)allocation and uses\u00a0constant stack space. Of course, such a calculus is only relevant if we can\u00a0express interesting algorithms; we provide numerous examples of in-place\u00a0functions on datastructures such as splay trees or finger trees, together with\u00a0in-place versions of merge sort and quick sort.\u00a0We also show how we can generically derive a map function over _any_ polynomial\u00a0data type that is fully in-place. Finally, we have implemented the rules of the\u00a0FIP calculus in the Koka language. Using the Perceus reference counting garbage collection, this implementation dynamically executes FIP functions in-place\u00a0whenever possible.<\/p>\n","protected":false},"excerpt":{"rendered":"
As functional programmers we always face a dilemma: should we write purely\u00a0functional code, or sacrifice purity for efficiency and resort to in-place\u00a0updates? This paper identifies precisely when we can have the best of both\u00a0worlds: a wide class of purely functional programs can be executed safely using in-place updates without requiring allocation, provided their arguments are 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