Some extensions of the basic formalism of stream processing functions are useful to specify complex structures such as operating systems. In this paper we give the foundations of higher order stream processing functions. These are functions which send and accept not only messages representing atomic data, but also complex elements such as functions. Some special notations are introduced for the specification and manipulation of such functions. A representation of time is outlined, which enables us to model time dependent behaviour. Finally, we demonstrate how characteristic operating system structures can be modelled by timed higher order stream processing functions.

In the last two decades, category theory has become one of the main tools for the denotational investigation of programming languages. Taking advantage of the algebraic nature of the categorical semantics, and of the rewriting systems it suggests, it is possible to use these denotational descriptions as a base for research into more operational aspects of programming languages.

This approach proves to be particularly interesting in the study and the definition of environment machines for functional languages. The reason is that category theory offers a simple and uniform language for handling terms and environments (substitutions), and for studying their interaction (through application).

Several examples of known machines are discussed, among which the Categorical Abstract Machine of Cousineau et al. (1987) and Krivine's machine. Moreover, as an example of the power and fruitfulness of this approach, we define two original categorical machines. The first one is a variant of the CAM implementing a λ-calculus with both call-by-value and call-by-name as parameters passing modes. The second one is a variant of Krivine's machine performing complete reduction of λ-terms.

This paper compares three different sparse matrix representations in Miranda for solving linear systems of equations: quadtrees, binary trees and run-length encoding. It compares the three data structures in each of two common linear system solvers, Conjugate Gradient and SOR. The test problems used in the paper arise from a simple reservoir model.

The G-machine (Johnsson, 1987; Peyton Jones, 1987) is a compiled graph reduction machine for lazy functional languages. The G-machine compiler contains many optimizations to improve performance. One set of such optimizations is designed to improve the performance of tail recursive functions. Unfortunately, the abstract machine is subject to a space leak—objects are unnecessarily preserved by the garbage collector. This paper analyses why a particular form of space leak occurs in the G-machine, and presents some ideas for fixing this problem. This phenomena in other abstract machines is also examined briefly.

One can have all the advantages of functional programming – correctness, clarity, simplicity, and flexibility – without any sacrifice in performance, even for a scientifically significant computation on a supercomputer. Therefore, why use Fortran? We demonstrate parity – equality of speed and storage use – between a program generated automatically from a functional specification and a program written by hand in the procedural style. To our knowledge, this demonstration of parity is the first for a program that solves a scientifically significant problem – quasi-linear hyperbolic partial differential equations – on a scientifically interesting supercomputer – the CRAY X-MP. We use pure Lisp, including higher-order functions, to express the functional specification for the PDE solver. We designed this specification for maximal clarity and flexibility, rather than for efficiency. Nevertheless, we obtain a highly efficient program to solve the PDEs: automated program transformations put back the missing efficiency as they produce an executable Fortran program from the specification. The generated Fortran program vectorizes on the CRAY X-MP and runs about 4% faster than a handwritten Fortran program for the same problem. We describe the problem and the specification, and some of the problem-domain-specific and hardware-specific transformations that we use to obtain the high-efficiency program.