Titlebook: Research Directions in Parallel Functional Programming; Kevin Hammond,Greg Michaelson Book 1999 Springer-Verlag London Limited 1999 algori

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Programming Language Constructs a high level of abstraction and thus support the task of program development substantially. Research in parallel functional programming also tries to provide these advantages in the context of parallel program development. Following the idea of declarative programming, the main task of a parallel p

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Realisations for Strict Languagesnacy of results. Thus they can also be evaluated non-sequentially., under the control of individual . (or .), in a system of several processing sites. All it takes to do so are some means to partition a program into concurrently executable (sub-)terms, and to compose, in reverse order, an evaluated

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Data Parallelismments of the data structure are performed simultaneously. In the simplest case, for example, this means that a loop over an array is replaced by a constant-time aggregate operation. In order to introduce parallelism, the programmer thinks about the organisation of data structures rather than the org

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Shaping Distributions and optimisation. While the FIS. language [196] shows that significant benefits accrue in the sequential setting, an even greater impact is anticipated in parallel, as shape theory is able to address some of the key issues exposed in earlier chapters. Let us consider these issues first, before gett

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Performance Monitoring available hardware. Most parallel functional languages hide this information from the programmer, providing very simple annotations to control the parallelism in a program [476], [288], [573], [104]. Some, such as Hudak’s . programming [288], allow the programmer to describe the mapping of tasks on