Simon B. Jones

Extending Deforestation for First Order functional Programs

Intermediate data structures are widely used in functional programs. Programs which use these intermediate structures are usually a lot easier to understand, but they result in loss of efficiency at run time. In order to reduce these run-time costs, a transformation algorithm called deforestation was proposed by Wadler which could eliminate intermediate structures. However, this algorithm is only applicable to a subset of all first-order functional terms. In this paper, it is shown how deforestation can be extended to be made applicable to all first-order functional terms. This is achieved by performing static analysis to determine which intermediate data structures can be eliminated. Using this extended form of deforestation, the majority of garbage which can be detected at compile-time will not be generated at run-time.

Compile-Time Garbage Collection by Necessity Analysis

This paper gives a brief overview of an analysis technique for determining at compile-time whether heap cells are still needed at a particular point in the execution of a functional program. This technique involves the analysis of necessity information. The analysis technique is used to determine at compile-time which heap cells can be deallocated, and also which of these deallocated cells can subsequently be reallocated.

Enabling Quantitative Data Analysis Through e-Infrastructure

This article discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities that are central to quantitative data analysis, referred to as ‘‘data management,’’ can benefit from e-Infrastructural support. We conclude by discussing how these issues are relevant to the Data Management through e-Social Science (DAMES) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences.

A social science data-fusion tool and the Data Management through e-Social Science (DAMES) infrastructure

The last two decades have seen substantially increased potential for quantitative social science research. This has been made possible by the significant expansion of publicly available social science datasets, the development of new analytical methodologies, such as microsimulation, and increases in computing power. These rich resources do, however, bring with them substantial challenges associated with organizing and using data. These processes are often referred to as ‘data management’. The Data Management through e-Social Science (DAMES) project is working to support activities of data management for social science research. This paper describes the DAMES infrastructure, focusing on the data-fusion process that is central to the project approach. It covers: the background and requirements for provision of resources by DAMES; the use of grid technologies to provide easy-to-use tools and user front-ends for several common social science data-management tasks such as data fusion; the approach taken to solve problems related to data resources and metadata relevant to social science applications; and the implementation of the architecture that has been designed to achieve this infrastructure.

Is Compile Time Garbage Collection Worth the Effort

For some time one of us, Jones, has been studying the problem of the compile time garbage collection of dynamic data structures in functional languages; this work is in collaboration with Daniel Le Metayer (IRISA, Rennes, France), and has already been described in [1, 2]. The work described in these papers, and in others in the area (for example, [3]), is largely a theoretical study of the abstract interpretation techniques which might be applied to compile time garbage collection and related problems.

The Implementer’s Dilemma: A Mathematical Model of Compile Time Garbage Collection

Optimization by compile time garbage collection is one possible weapon in the functional language implementer’s armoury for combatting the excessive memory allocation usually exhibited by functional programs. It is an interesting idea, but the practical question of whether it yields benefits in practice has still not been answered convincingly one way or the other.

Managing Data in E-Social Science

Grid computing is moving from its original focus on the physical sciences to other disciplines such as the social sciences. The orientation of these newer applications is on data management rather than processing. This paper describes how the DAMES project (Data Management through E-Social Science) is developing grid-based solutions for handling data in a distributed environment. The paper describes the approach being taken to meet key challenges: metadata for effective use of datasets, and data-oriented workflows for e-social science.

A new method for strictness analysis on non-flat domains

The authors address the problem of strictness analysis, particularly in the case of non-flat domains, i.e., lazily evaluated lists. Their approach to this problem is novel in that it is based on an abstract domain of very general necessity patterns which allow the requirements that a function has for the components of the data structures which form its arguments, to be modeled in great detail. The necessity patterns also allow the generation of finite abstract domains in a systematic way that can be easily customized for the application. The authors refer to the analysis using necessity patterns as a necessity analysis. This is essentially a form of backwards analysis but carried out in a different framework.<<ETX>>