Yike Guo

Parallel skeletons for structured composition

In this paper, we propose a straightforward solution to the problems of compositional parallel programming by using skeletons as the uniform mechanism for structured composition. In our approach parallel programs are constructed by composing procedures in a conventional base language using a set of high-level, pre-defined, functional, parallel computational forms known as skeletons. The ability to compose skeletons provides us with the essential tools for building further and more complex application-oriented skeletons specifying important aspects of parallel computation. Compared with the process network based composition approach, such as PCN, the skeleton approach abstracts away the fine details of connecting communication ports to the higher level mechanism of making data distributions conform, thus avoiding the complexity of using lower level ports as the means of interaction. Thus, the framework provides a natural integration of the compositional programming approach with the data parallel programming paradigm.

Functional Skeletons for Parallel Coordination

In this paper we propose a methodology for structured parallel programming using functional skeletons to compose and coordinate concurrent activities written in a standard imperative language. Skeletons are higher order functional forms with built-in parallel behaviour. We show how such forms can be used uniformly to abstract all aspects of a parallel programs behaviour including data partitioning, placement and re-arrangement (communication) as well as computation. Skeletons are naturally data parallel and are capable of expressing computation and co-ordination at a higher level of abstraction than other process oriented co-ordination notations. Examples of the application of this methodology are given and an implementation technique outlined.

Co-ordinating Heterogeneous Parallel Computation

There is a growing interest in heterogeneous high performance computing environments. These systems are difficult to program owing to the complexity of choosing the appropriate resource allocations and the difficulties in expressing these choices in traditional parallel languages. In this paper we propose that functional skeletons are used to express these resource allocation strategies. By associating performance models with each skeleton it is possible to predict and optimise the performance of different resource allocation strategies, thus providing a tool for guiding the choice of resource allocation. Through a case study of a parallel conjugate gradient algorithm on a mixed vector and scalar parallel machine we demonstrate these features of the SPP(X) approach.

Parallel Induction Algorithms for Data Mining

In the last decade, there has been an explosive growth in the generation and collection of data. Nonetheless, the quality of information inferred from this voluminous data has not been proportional to its size. One of the reasons for this is that the computational complexities of the algorithms used to extract information from the data are normally proportional to the number of input data items resulting in prohibitive execution time on large data sets. Parallelism is one solution to this problem. In this paper we present preliminary results on experiments in parallelising C4.5, a classification-rule learning system using decision-trees as a model representation, which has been used as a base model for investigating methods for parallelising induction algorithms. The experiments assess the potential for improving the execution time by exploiting parallelism in the algorithm.

GOFFIN: higher-order functions meet concurrent constraints

Abstract We introduce a higher-order constraint-based language for structured and declarative parallel programming. The language, called Goffin , systematically integrates constraints and user-defined functions within a uniform setting of concurrent programming. From the perspective of parallel programming methodology, the constraint part of Goffin provides a co-ordination language for the functional part, which takes on the role of the computation language. This conceptual distinction allows the structured formulation of parallel algorithms. Goffin is an extension of the purely functional language Haskell. The functional kernel is embedded in a layer based on concurrent constraints. Logical variables are bound by constraints which impose relations over expressions that may contain user-defined functions. Referential transparency is preserved by restricting the creation of logical variables to the constraint part and by suspending the reduction of functional expressions that depend on the value of an unbound logical variable. Hence, constraints are the means to organize the concurrent reduction of functional expressions. Moreover, constraint abstractions, i.e., functions over constraints, allow the definition of parameterized co-ordination forms. In correspondence with the higher-order nature of the functional part, abstractions in the constraint logic part are based on higher-order logic, leading to concise and modular specifications of behaviour. We introduce and explain Goffin together with its underlying programming methodology, and present a declarative as well as an operational semantics for the language. To formalize the semantics, we identify the essential core constructs of the language and characterize their declarative meaning by associating them with formulae of Churchs simple theory of types. We also present a reduction system that captures the concurrent operational semantics of the core constructs. In the course of this paper, the soundness of this reduction system with respect to the declarative semantics is established.

Parallelizing Conditional Recurrences

Recursive functions which use conditional constructs are common in functional (and imperative) programs. We present a collection of techniques for handling such functions for a parallel synthesis method. These techniques can help us enlarge the class of sequential functions which could be systematically transformed to parallel equivalent.

Constraint Logic Programming in the Sequent Calculus

In this paper, we are developing a new logical semantics of CLP. It is shown that CLP is based on an amalgamated logic embedding the entailment relation of constraints into a fragment of intuitionistic logic. Constrained SLD resolution corresponds to a complete proof search in the amalgamated logic. The framework provides not only the logical account on the definitional semantics towards CLP but also a general way to integrate constraints into various logic programming systems.

The minimised geometric Buchberger algorithm: an optimal algebraic algorithm for integer programming

IP problems characterise combinatorial optimisation problems where conventional numerical methods based on the hill-climbing technique can not be directly applied. Conventional methods for solving integer programming are based on searching algorithms where heuristics such as branch and bound are applied to reduce the search space. Recently, various algebraic IP solvers have been proposed based on the theory of Grx7fobner bases. The key idea is to encode an IP problem IPA;C into a special ideal associated with the constraint matrix A and the cost (object) function C. An important property of such an encoding is that its Grx7fobner basis corresponds directly to the test set of the IP problem. The main di culty of these new methods is the size of the Grx7fobner bases generated. In the proposed algorithms, large Grx7fobner bases are caused by either introducing additional variables or by considering the generic IP problem IPA;C . Some improvements have been proposed such as the Hosten and Sturmfels method (GRIN) designed to avoid additional variables and the truncated Grx7fobner basis method of Thomas which computes the Grx7fobner basis for a speci c IP problem IPA;C(b) (rather than its generalisation IPA;C). In this paper we propose a new algebraic algorithm for solving integer programming problems. The new algorithm, called the Minimised Geometric Buchberger Algorithm (MGBA), combines the Hosten and Sturmfels method(GRIN) and Thomass truncated GBA to compute the fundamental segments of a IP problem IPA;C directly in its original space and also the truncated Grx7fobner basis for a speci c IP problem IPA;C (b). We have carried out experiments to compare this algorithm with others such as the geometric Buchberger algorithm, the truncated geometric Buchberger algorithm, and the algorithm in GRIN. These experiments shows that the new algorithm o ers signi cant performance improvement.

Functional Programming Languages with Logical Variables: A Linear Logic View

The idea of introducing logical variables into functional programming languages has been proposed for some years, and many concrete languages with this feature have been developed. Recently the semantic features of the languages in this paradigm have been uniformly modelled using the constraint computation formalism [RJK92]. However, the operational behaviour of these languages has not been studied systematically. In this paper, we propose a linear logic formalization of the the computational behaviour of functional programming languages with logical variables. It is shown that, with the resource-consciousness of linear logic, the computation of the paradigm can be uniformly modelled as deduction in the logic. Therefore, the model provides the logical meaning for the computation as well as a general abstract machine for the implementation of these languages.