Theory and practice of demand analysis in haskell

Abstract This paper presents the fruits of a decade-long experience with strictness analysis, in the context of the Glasgow Haskell Compiler, an optimising compiler for Haskell

Granularity in Large-Scale Parallel Functional Programming

This thesis demonstrates how to reduce the runtime of large non-strict functional programs using parallel evaluation. The parallelisation of several programs shows the importance of granularity, i.e. the computation costs of program expressions. The aspect of granularity is studied both on a practical level, by presenting and measuring runtime granularity improvement mechanisms, and at a more formal level, by devising a static granularity analysis. By parallelising several large functional programs this thesis demonstrates for the first time the advantages of combining lazy and parallel evaluation on a large scale: laziness aids modularity, while parallelism reduces runtime. One of the parallel programs is the Lolita system which, with more than 47,000 lines of code, is the largest existing parallel non-strict functional program. A new mechanism for parallel programming, evaluation strategies, to which this thesis contributes, is shown to be useful in this parallelisation. Evaluation strategies simplify parallel programming by separating algorithmic code from code specifying dynamic behaviour. For large programs the abstraction provided by functions is maintained by using a data-oriented style of parallelism, which defines parallelism over intermediate data structures rather than inside the functions. A highly parameterised simulator, GRANSIM, has been constructed collaboratively and is discussed in detail in this thesis. GRANSIM is a tool for architecture-independent parallelisation and a testbed for implementing runtime-system features of the parallel graph reduction model. By providing an idealised as well as an accurate model of the underlying parallel machine, GRANSIM has proven to be an essential part of an integrated parallel software engineering environment. Several parallel runtime- system features, such as granularity improvement mechanisms, have been tested via GRANSIM. It is publicly available and in active use at several universities worldwide. In order to provide granularity information this thesis presents an inference-based static granularity analysis. This analysis combines two existing analyses, one for cost and one for size information. It determines an upper bound for the computation costs of evaluating an expression in a simple strict higher-order language. By exposing recurrences during cost reconstruction and using a library of recurrences and their closed forms, it is possible to infer the costs for some recursive functions. The possible performance improvements are assessed by measuring the parallel performance of a hand-analysed and annotated program

Projection-Based Program Analysis

Projection-based program analysis techniques are remarkable for their ability to give highly detailed and useful information not obtainable by other methods. The first proposed projection-based analysis techniques were those of Wadler and Hughes for strictness analysis, and Launchbury for binding-time analysis; both techniques are restricted to analysis of first-order monomorphic languages. Hughes and Launchbury generalised the strictness analysis technique, and Launchbury the binding-time analysis technique, to handle polymorphic languages, again restricted to first order. Other than a general approach to higher-order analysis suggested by Hughes, and an ad hoc implementation of higher-order binding-time analysis by Mogensen, neither of which had any formal notion of correctness, there has been no successful generalisation to higher-order analysis. We present a complete redevelopment of monomorphic projection-based program analysis from first principles, starting by considering the analysis of functions (rather than programs) to establish bounds on the intrinsic power of projection-based analysis, showing also that projection-based analysis can capture interesting termination properties. The development of program analysis proceeds in two distinct steps: first for first-order, then higher order. Throughout we maintain a rigorous notion of correctness and prove that our techniques satisfy their correctness conditions. Our higher-order strictness analysis technique is able to capture various so-called data-structure-strictness properties such as head strictness-the fact that a function may be safely assumed to evaluate the head of every cons cell in a list for which it evaluates the cons cell. Our technique, and Hunt's PER-based technique (originally proposed at about the same time as ours), are the first techniques of any kind to capture such properties at higher order. Both the first-order and higher-order techniques are the first projection-based techniques to capture joint strictness properties-for example, the fact that a function may be safely assumed to evaluate at least one of several arguments. The first-order binding-time analysis technique is essentially the same as Launchbury's; the higher-order technique is the first such formally-based higher-order generalisation. Ours are the first projection-based termination analysis techniques, and are the first techniques of any kind that are able to detect termination properties such as head termination-the fact that termination of a cons cell implies termination of the head. A notable feature of the development is the method by which the first-order analysis semantics are generalised to higher-order: except for the fixed-point constant the higher-order semantics are all instances of a higher-order semantics parameterised by the constants defining the various first-order semantics

Implementing Projection-based Strictness Analysis

Projection-based backwards strictness analysis has been understood for some years. Surprisingly, even though the method is fairly simple and quite general, no reports of its implementation have appeared. This paper describes ideas underlying our prototype implementation of the analysis for a simple programming language. The implementation serves as a case study before applying the method in the Glasgow Haskell compiler. 1 Introduction The method of projection-based backwards strictness analysis for first-order, lazy functional languages was first presented by Wadler and Hughes [8] in 1987. Since then it has been generalised by Hughes [4] and Hughes and Launchbury [3] to work for user-defined types and for polymorphism. Yet, to our knowledge, it has never been implemented even though the method is fairly simple and quite general. The time has come for projection-based strictness analysis to meet practice. This paper describes a prototype implementation. Initially we expected that build..