Many parallel algorithms are naturally expressed at a fine level of granularity, often finer than a MIMD parallel system can exploit efficiently. Most builders of parallel systems have looked to either the programmer or a parallelizing compiler to increase the granularity of such algorithms. In this paper we explore a third approach to the granularity problem by analyzing two strategies for combining parallel tasks dynamically at run-time. We reject the simpler load-based inlining method, where tasks are combined based on dynamic load level, in favor of the safer and more robust lazy task creation method, where tasks are created only retroactively as processing resources become available. These strategies grew out of work on Mul-T [15], an efficient parallel implementation of Scheme, but could be used with other languages as well. We describe our Mul-T implementations of lazy task creation for two contrasting machines, and present performance statistics which show the method's effectiveness. Lazy task creation allows efficient execution of naturally expressed algorithms of a substantially finer grain than possible with previous parallel Lisp systems.

this report was partially supported by Grant CCR-90-24549 from the National Science Foundation. This is a report to the National Science Foundation and other agencies; it is not a report by or of the National Science Foundation or any other agency. Participants at the Workshop on Research Directions in Integrating Numerical Analysis, Symbolic Computing, Computational Geometry, and Artificial Intelligence for Computational Science Conference Organizers

In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The state-of-the-art is summarized along with sources (books, bibliographics, web sites) of more detailed information. The principal components and paradigms for building PSEs are presented. The discussion of the future is given in three parts: future trends, scenarios for 2010/2025, and research

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We describe the design and implementation of pD, a parallel variant of the small functional language D. In particular, we show the compilation techniques that we apply to translate a pD program with para-functional annotations into statically typed parallel C code that corresponds (with restrictions) in structure and quality to a program written by a human programmer. The generated code can be linked with the PACLIB kernel, the multi-processor runtime system for the computer algebra library SACLIB. This allows to develop parallel computer algebra applications in an elegant and efficient manner. Supported by the FWF grant S5302-PHY "Parallel Symbolic Computation". Contents 1 Introduction 4 2 The Pseudo-Functional Core Language 6 2.1 Abstract Syntax : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.2 Examples : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 2.3 Semantic Algebras : : : : : : : : : : : : : : : : : : : : : : : : : : : : :...

We present two methods for building a finite-state transducer which generalizes a finitestate transduction to any word on a given alphabet. The methods are exact in the sense that the inferred transducer coincides on the inputs for which the initial function is defined. We apply the methods to the problem of grapheme-to-phoneme transcription.

This paper surveys sequential and parallel implementation techniques for functional programming languages, as well as optimizations that can improve their performance. Sequential implementations have evolved from simple interpreters to sophisticated super-combinator-based compilers, while most parallel implementations have explored a broad range of techniques. We analyze the purpose and function of each implementation technique and discuss the current state-of-the-art in functional language implementation.