The views and conclusions contained in this document are those of the authors and should not be interpreted as

In the past 20 years there has been treftlendous progress in developing and analyzing parallel algorithftls. Researchers have developed efficient parallel algorithms to solve most problems for which efficient sequential solutions are known. Although some ofthese algorithms are efficient only in a theoretical framework, many are quite efficient in practice or have key ideas that have been used in efficient implementations. This research on parallel algorithms has not only improved our general understanding ofparallelism but in several cases has led to improvements in sequential algorithms. Unf:ortunately there has been less success in developing good languages f:or prograftlftling parallel algorithftls, particularly languages that are well suited for teaching and prototyping algorithms. There has been a large gap between languages

The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Wright Laboratory or the U. S. Government. Keywords: Data-parallel, parallel algorithms, supercomputers, nested parallelism, This report describes Nesl, a strongly-typed, applicative, data-parallel language. Nesl is intended to be used as a portable interface for programming a variety of parallel and vector computers, and as a basis for teaching parallel algorithms. Parallelism is supplied through a simple set of data-parallel constructs based on sequences, including a mechanism for applying any function over the elements of a sequence in parallel and a rich set of parallel functions that manipulate sequences. Nesl fully supports nested sequences and nested parallelism—the ability to take a parallel function and apply it over multiple instances in parallel. Nested parallelism is important for implementing algorithms with irregular nested loops (where the inner loop lengths depend on the outer iteration) and for divide-and-conquer algorithms. Nesl also provides a performance model for calculating the asymptotic performance of a program on

Many high-level parallel programming languages allow for fine-grained parallelism. As in the popular work-time framework for parallel algorithm design, programs written in such languages can express the full parallelism in the program without specifying the mapping of program tasks to processors. A common concern in executing such programs is to schedule tasks to processors dynamically so as to minimize not only the execution time, but also the amount of space (memory) needed. Without careful scheduling, the parallel execution on p processors can use a factor of p or larger more space than a sequential implementation of the same program. This paper first identifies a class of parallel schedules that are provably efficient in both time and space. For any

In this paper we prove time and space bounds for the implementation of the programming language NESL on various parallel machine models. NESL is a sugared typed J-calculus with a set of array primitives and an explicit parallel map over arrays. Our results extend previous work on provable implementation bounds for functional languages by considering space and by including arrays. For modeling the cost of NESL we augment a standard call-by-value operational semantics to return two cost measures: a DAG representing the sequential dependence in the computation, and a measure of the space taken by a sequential implementation. We show that a NESL program with w work (nodes in the DAG), d depth (levels in the DAG), and s sequential space can be implemented on a p processor butterfly network, hypercube, or CRCW PRAM usin O(w/p + d log p) time and 0(s + dp logp) reachable space. For programs with sufficient parallelism these bounds are optimal in that they give linew speedup and use space within a constant factor of the sequential space. 1

We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programmingmodel and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency. Our current aim is to provide a convenient programming environment for SMP parallelism, and especially multicore architectures. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups.

The process of writing large parallel programs is complicated by the need to specify both the parallel behaviour of the program and the algorithm that is to be used to compute its result. This paper introduces evaluation strategies, lazy higher-order functions that control the parallel evaluation of non-strict functional languages. Using evaluation strategies, it is possible to achieve a clean separation between algorithmic and behavioural code. The result is enhanced clarity and shorter parallel programs. Evaluation strategies are a very general concept: this paper shows how they can be used to model a wide range of commonly used programming paradigms, including divideand -conquer, pipeline parallelism, producer/consumer parallelism, and data-oriented parallelism. Because they are based on unrestricted higher-order functions, they can also capture irregular parallel structures. Evaluation strategies are not just of theoretical interest: they have evolved out of our experience in parallelising several large-scale applications, where they have proved invaluable in helping to manage the complexities of parallel behaviour. These applications are described in detail here. The largest application we have studied to date, Lolita, is a 60,000 line natural language parser. Initial results show that for these applications we can achieve acceptable parallel performance, while incurring minimal overhead for using evaluation strategies.

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this article we discuss the design goals and decisions that determined the final form of Jade and present an overview of the Jade implementation. We also present our experience using Jade to implement several complete scientific and engineering applications. We use this experience to evaluate how the different Jade language features were used in practice and how well Jade as a whole supports the process of developing parallel applications. We find that the basic idea of preserving the serial semantics simplifies the program development process, and that the concept of using data access specifications to guide the parallelization offers significant advantages over more traditional control-based approaches. We also find that the Jade data model can interact poorly with concurrency patterns that write disjoint pieces of a single aggregate data structure, although this problem arises in only one of the applications. Categories and Subject Descriptors: D.1.3 [Programming Te

This paper describes the design and implementation of a practical parallel algorithm for Delaunay triangulation that works well on general distributions. Although there have been many theoretical parallel algorithms for the problem, and some implementations based on bucketing that work well for uniform distributions, there has been little work on implementations for general distributions. We use the well known reduction of 2D Delaunay triangulation to find the 3D convex hull of points on a paraboloid. Based on this reduction we developed a variant of the Edelsbrunner and Shi 3D convex hull algorithm, specialized for the case when the point set lies on a paraboloid. This simplification reduces the work required by the algorithm (number of operations) from O(n log^2 n) to O(n log n). The depth (parallel time) is O(log^3 n) on a CREW PRAM. The algorithm is simpler than previous O(n log n) work parallel algorithms leading to smaller constants. Initial experiments using a variety of distributions showed that our parallel algorithm was within a factor of 2 in work from the best sequential algorithm. Based on these promising results, the algorithm was implemented using C and an MPI-based toolkit. Compared with previous work, the resulting implementation achieves significantly better speedups over good sequential code, does not assume a uniform distribution of points, and is widely portable due to its use of MPI as a communication mechanism. Results are presented for the IBM SP2, Cray T3D, SGI Power Challenge, and DEC AlphaCluster.