Have you read your PFDS today?

Purely Functional Data Structures
http://www.cs.cmu.edu/~rwh/theses/okasaki.pdf

When a C programmer needs an efficient data structure for a particular problem, he or she can often simply look one up in any of a number of good textbooks or handbooks. Unfortunately, programmers in functional languages such as Standard ML or Haskell do not have this luxury. Although some data structures designed for imperative languages such as C can be quite easily adapted to a functional setting, most cannot, usually because they depend in crucial ways on assignments, which are disallowed, or at least discouraged, in functional languages. To address this imbalance, we describe several techniques for designing functional data structures, and numerous original data structures based on these techniques, including multiple variations of lists, queues, double-ended queues, and heaps, many supporting more exotic features such as random access or efficient catenation.

In addition, we expose the fundamental role of lazy evaluation in amortized functional data structures. Traditional methods of amortization break down when old versions of a data structure, not just the most recent, are available for further processing. This property is known as persistence, and is taken for granted in functional languages. On the surface, persistence and amortization appear to be incompatible, but we show how lazy evaluation can be used to resolve this conflict, yielding amortized data structures that are efficient even when used persistently. Turning this relationship between lazy evaluation and amortization around, the notion of amortization also provides the first practical techniques for analyzing the time requirements of non-trivial lazy programs.
 
Finally, our data structures offer numerous hints to programming language designers, illustrating the utility of combining strict and lazy evaluation in a single language, and providing non-trivial examples using polymorphic recursion and higher-order, recursive modules.

>>73
Not many people are working on that stack, but that is what I'm getting at and what I'm looking for. No, >>71 is shit.

>>71
Just because there is noise at runtime doesn't mean you can't produce programs that will on average greatly outperform other programs on average. The statement GC is or is not slow depends greatly on how you are using it and what the alternatives are. But if you can avoid it altogether, that's one less thing your program needs to spend time doing, and if you take more control over how things are allocated in memory you can arrange objects compactly to save memory and adjacently to improve cache performance.