Abstract
We discuss why existing implementations of functional languages on MIMD-machines with distributed memory are slow. This is done by comparing the behavior of a functional program with a corresponding Occam program. The main reason is that functional languages give insufficient means to control parallelism and communication. Our approach is to support data parallelism by providing a set of primitives on arrays which allow the user to control the parallelism and communication on a high level, disabling problems like deadlocks. Only one unique version of an array may be referenced at a time. This restriction allows arrays to be updated in place and enables the user to control the space requirements of the program. The uniqueness of arrays is checked by the compiler. Experimental results demonstrate the efficiency of our data parallel functional language.KeywordsParallel ImplementationVersion NumberFunctional LanguageGlobal SynchronizationReference CountThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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