Optimizing Analysis for First-Class Tuple-Spaces

This paper considers the design and optimization of a simple asynchronous parallel language that uses rst-class tuple-spaces as its main communication and process creation device. Our proposed kernel language diiers from other tuple-space languages insofar tuple-spaces are treated as true rst-class objects. Moreover, we develop a formal framework for constructing an optimizing pre-processor for such a language. The semantic analysis is based on an inference engine that statically computes the set of tuples (and their structural attributes) that can occupy any given tuple-space. The inference system is non-trivial insofar as it operates in the presence of higher-order functions and non-at data structures (e.g, lists). The result of the inference procedure can be used to customize the representation of tuple-space objects.