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 su ers from two problems. First, the main parallel data structures that most data par...

This paper presents a system to produce e cient implementations of parallel array-based algorithms from high-level speci cations. It is structured as a transformation through a series of progressively more detailed representations. This allows the use of high-level programming features without losing the ne control of low-level languages. During the transformation process, parallel implementati...

A visit to the neighborhood PC retail store provides ample proof that we are in the multi-core era. The key differentiator among manufacturers today is the number of cores that they pack onto a single chip. The clock frequency of commodity processors has reached its limit, however, and is likely to stay below 4 GHz for years to come. As a result, adding cores is not synonymous with increasing c...

The advantages of pattern-based programming have been well-documented in the sequential literature. However patterns have yet to make their way into mainstream parallel computing, even though several research tools support them. There are two critical shortcomings of pattern (or template) based systems for parallel programming: lack of extensibility and performance. The patterns supported by a ...

In this workshop session, three speakers present their viewpoints and contributions to the topic of portable parallel programming languages. They are Dennis Gannon from Indiana University, David Loveman from Digital Equipment Corporation, and David Padua from the University of Illinois. Their presentations discuss extensions to C, Fortran, and compiler support for standard languages on parallel...

Traditional parallel programming styles have many problems which hinder the development of parallel applications. The message passing style can be too complex for many programmers. While shared memory based parallel programming is relatively easy, it requires programmers to guarantee there is no data race in programs. Data race conditions are generally difficult to debug and difficult to preven...

This paper describes Parallel Distributed Genetic Programming (PDGP), a new form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of paral-lelism and an eecient and eeective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the ow of control and results. In ...

Ever-increasing demand for computing capability is driving the construction of ever-larger computer clusters, soon to be reaching tens of thousands of processors. Many functionalities of system software have failed to scale accordingly–systems are becoming more complex, less reliable, and less efficient. Our premise is that these deficiencies arise from a lack of global control and coordination...

We study the parallel computation of dynamic programming. We consider four important dynamic programming problems which have wide application, and that have been studied extensively in sequential computation: (1) the 1D problem, (2) the gap problem, (3) the parenthesis problem, and (4) the RNA problem. The parenthesis problem has fast parallel algorithms; almost no work has been done for parall...

This Paper describes the research centered on the Charm Parallel Programming System. Charm is a portable parallel programming system under development at the University of Illinois for the past six years. The system enhances latency tolerance via message driven execution, supports dynamic load balancing for medium grain tasks, and provides innovative language features such as speciically shared...