Parallel skeletons intend to encourage programmers to build...

Parallel primitives (skeletons) intend to encourage programmers to build a parallel program from ready-made components for which efficient implementations are known to exist, making the parallelization process easier. However, programmers often suffer from the difficulty to choose a combination of proper parallel primitives so as to construct efficient parallel programs. To overcome this difficulty, we shall propose a new transformation, called diffusion, which can efficiently decompose a recursive definition into several functions such that each function can be described by some parallel primitive. This allows programmers to describe algorithms in a more natural recursive form. We demonstrate our idea with several interesting examples. Our diffusion transformation should be significant not only in development of new parallel algorithms, but also in construction of parallelizing compilers.

Programming with parallel skeletons is an attractive framework because it encourages programmers to develop efficient and portable parallel programs. However, extracting parallelism from sequential specifications and constructing efficient parallel programs using the skeletons are still difficult tasks. In this paper, we propose an analytical approach to transforming recursive functions on general recursive data structures into compositions of parallel skeletons. Using static slicing, we have defined a classification of subexpressions based on their data-parallelism. Then, skeleton-based parallel programs are generated from the classification. To extend the scope of parallelization, we have adopted more general parallel skeletons which do not require the associativity of argument functions. In this way, our analytical method can parallelize recursive functions with complex data flows. Keywords: data parallelism, parallelization, functional languages, parallel skeletons, data flow analysis, static slice 1.

Document archives contain large amounts of data to which sophisticated queries are applied. The size of archives and the complexity of evaluating queries makes the use of parallelism attractive. The use of semantically-based markup such as SGML makes it possible to represent documents and document archives as data types. We present a theory of trees and tree homomorphisms, modelling structured text archives and operations on them, from which it can be seen that: ffl many apparently-unrelated tree operations are homomorphisms; ffl homomorphisms can be described in a simple parameterised way that gives standard sequential and parallel implementations for them; ffl special classes of homomorphisms have parallel implementations of practical interest. In particular, we develop an implementation for path expression search, a novel powerful query facility for structured text, that takes time logarithmic in the text size. Keywords: structured text, categorical data type, software developme...

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While tree contraction algorithms play an important role in e#cient tree computation in parallel, it is di#cult to develop such algorithms due to the strict conditions imposed on contracting operators. In this paper, we propose a systematic method of deriving e#cient tree contraction algorithms from recursive functions on trees in any shape. We identify a general recursive form that can be parallelized to obtain e#cient tree contraction algorithms, and present a derivation strategy for transforming general recursive functions to parallelizable form. We illustrate our approach by deriving a novel parallel algorithm for the maximum connected-set sum problem on arbitrary trees, the tree-version of the famous maximum segment sum problem.

Data parallelism is currently one of the most successful models for programming massively parallel computers. The central idea is to evaluate a uniform collection of data in parallel by simultaneously manipulating each data element in the collection. Despite many of its promising features, the current approach suffers from two problems. First, the main parallel data structures that most data parallel languages currently support are restricted to simple collection data types like lists, arrays or similar structures. But other useful data structures like trees have not been well addressed. Second, parallel programming relies on a set of parallel primitives that capture parallel skeletons of interest. However, these primitives are not well structured, and efficient parallel programming with these primitives is difficult. In this paper, we propose a polytypic framework for developing efficient parallel programs on most data structures. We showhow a set of polytypic parallel primitives can be formally defined for manipulating most data structures, how these primitives can be successfully structured into a uniform recursive definition, and how an efficient combination of primitives can be derived from a naive specification program. Our framework should be significant not only in development of new parallel algorithms, but also in construction of parallelizing compilers.

This paper introduces accumulation into list homomorphisms for systematic development of both efficient and correct parallel programs. New parallelizable recursive pattern called is given, and transformations from sequential patterns in the form into (H-)homomorphism are shown. We illustrate the power of our formalization by developing a novel and general parallel program for a class of interesting and challenging problems, known as maximum marking problems. 1.

We describe a methodology for modelling the performance of parallel algorithmic skeletons and illustrate it using a new skeleton which supports set membership classification problems in Constructive Solid Geometry. These problems are characterised by potentially unbalanced tree structures with data-dependent computations at the nodes. The skeleton achieves good parallel efficiency even with highly unbalanced trees and, with some restrictions on the class of computations permitted, the methodology accurately predicts the asymptotic performance of specific CSG applications. 1 Introduction In recent years several researchers (eg [1, 2]) have proposed the use of algorithmic skeletons as a way to resolve the complexity and machinedependence of programming parallel computers. A skeleton is a template providing an implementation of a class of parallel computationss which share a common structure. Well-known examples include pipelines, farms, and divide-and-conquer algorithms. A programmer n...

and there currently exist a wide selection of parallel programming languages and environments. This thesis presents and examines the Hierarchical Skeleton Model (HSM), a model of parallel programming that combines ease of use, portability and flexibility.