Abstract Over 25 implementations of different functional languages are benchmarked using the same program, a floating-point intensive application taken from molecular biology. The principal aspects studied are compile time and execution time for the various implementations that were benchmarked. An important consideration is how the program can be modified and tuned to obtain maximal performance on each language implementation. With few exceptions, the compilers take a significant amount of time to compile this program, though most compilers were faster than the then current GNU C compiler (GCC version 2.5.8). Compilers that generate C or Lisp are often slower than those that generate native code directly: the cost of compiling the intermediate form is normally a large fraction of the total compilation time. There is no clear distinction between the runtime performance of eager and lazy implementations when appropriate annotations are used: lazy implementations have clearly come of age when it comes to implementing largely strict applications, such as the Pseudoknot program. The speed of C can be approached by some implementations, but to achieve this performance, special measures such as strictness annotations are required by non-strict implementations. The benchmark results have to be interpreted with care. Firstly, a benchmark based on a single program cannot cover a wide spectrum of ‘typical’ applications. Secondly, the compilers vary in the kind and level of optimisations offered, so the effort required to obtain an optimal version of the program is similarly varied.

We present the extensional polymorphism, a framework to type check ad hoc polymorphic functions. This formalism is compatible with parametric polymorphism, and supports a large class of functions defined by structural pattern matching on types.

We present a new approach to the polymorphic typing of data accepting in-place modification in ML-like languages.This approach is based on restrictions over type generalization, and a refined typing of functions.The type system given here leads to a better integration of imperative programming style with the purely applicative kernel of ML.In particular, generic functions that allocate mutable data can safely be given fully polymorphic types.We show the soundness of this type system, and give a type reconstruction algorithm.