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We describe a new compiler for Standard ML called TIL, that is based on four technologies: intensional polymorphism, tag-free garbage collection, conventional functional language optimization, and loop optimization. We use intensional polymorphism and tag-free garbage collection to provide specialized representations, even though SML is a polymorphic language. We use conventional functional language optimization to reduce the cost of intensional polymorphism, and loop optimization to generate good code for recursive functions. We present an example of TIL compiling an SML function to machine code, and compare the performance of TIL code against that of a widely used compiler, Standard ML of New Jersey.

Compilers for monomorphic languages, such as C and Pascal, take advantage of types to determine data representations, alignment, calling conventions, and register selection. However, these languages lack important features including polymorphism, abstract datatypes, and garbage collection. In contrast, modern programming languages such as Standard ML (SML), provide all of these features, but existing implementations fail to take full advantage of types. The result is that performance of SML code is quite bad when compared to C. In this thesis, I provide a general framework, called type-directed compilation, that allows compiler writers to take advantage of types at all stages in compilation. In the framework, types are used not only to determine efficient representations and calling conventions, but also to prove the correctness of the compiler. A key property of typedirected compilation is that all but the lowest levels of the compiler use typed intermediate languages. An advantage of this approach is that it provides a means for automatically checking the integrity of the resulting code. An important

We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programmingmodel and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency. Our current aim is to provide a convenient programming environment for SMP parallelism, and especially multicore architectures. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups.

We describe a facility for improving optimization of Haskell programs using rewrite rules. Library authors can use rules to express domain-specific optimizations that the compiler cannot discover for itself. The compiler can also generate rules internally to propagate information obtained from automated analyses. The rewrite mechanism is fully implemented in the released Glasgow Haskell Compiler. Our system is very simple, but can be effective in optimizing real programs. We describe two practical applications involving short-cut deforestation, for lists and for rose trees, and document substantial performance improvements on a range of programs. 1 Introduction Optimising compilers perform program transformations that improve the efficiency of the program. However, a compiler can only use relatively shallow reasoning to guarantee the correctness of its optimisations. In contrast, the programmer has much deeper information about the program and its intended behaviour. For example, a programmer may know that

In this thesis we present and analyse a set of automatic source-to-source program transformations that are suitable for incorporation in optimising compilers for lazy functional languages. These transformations improve the quality of code in many different respects, such as execution time and memory usage. The transformations presented are divided in two sets: global transformations, which are performed once (or sometimes twice) during the compilation process; and a set of local transformations, which are performed before and after each of the global transformations, so that they can simplify the code before applying the global transformations and also take advantage of them afterwards. Many of the local transformations are simple, well known, and do not have major effects on their own. They become important as they interact with each other and with global transformations, sometimes in non-obvious ways. We present how and why they improve the code, and perform extensive experiments wit...

The extension of Haskell with a built-in state monad combines mathematical elegance with operational efficiency: ffl Semantically, at the source language level, constructs that act on the state are viewed as functions that pass an explicit store data structure around. ffl Operationally, at the implementation level, constructs that act on the state are viewed as statements whose evaluation has the side-effect of updating the implicit global store in place. There are several unproven conjectures that the two views are consistent. Recently, we have noted that the consistency of the two views is far from obvious: all it takes for the implementation to become unsound is one judiciously-placed beta-step in the optimization phase of the compiler. This discovery motivates the current paper in which we formalize and show the correctness of the implementation of monadic state. For the proof, we first design a typed call-by-need language that models the intermediate language of the compiler, to...

ABSTRACT. If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. But that doesn’t make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming, especially as the number of processors increases. A particularly promising and well-studied approach to employing large numbers of processors is data parallelism. Blelloch’s pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). In this paper we describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. We focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism. 1

Object-oriented programming languages have always distinguished between "primitive" and "user-defined" data types, and in the case of languages like C++ and Java, the primitives are not even treated as objects, further fragmenting the programming model. The distinction is especially problematic when a particular programming community requires primitive-level support for a new data type, as for complex, intervals, fixed-point numbers, and so on.

We describe a type system and typed semantics that use a hierarchy of monads to describe and delimit a variety of effects, including non-termination, exceptions, and state, in a call-by-value functional language. The type system and semantics can be used to organize and justify avariety of optimizing transformations in the presence of effects. In addition, we describe a simple monad inferencing algorithm that computes the minimum effect for each subexpression of a program, and provides more accurate effects information than local syntactic methods.