Abstract

The use of distributed data structures in a logically-shared memory is a natural, readily-understood approach to parallel programming. The principal argument against such an approach for portable software has always been that efficient implementations could not scale to massively-parallel, distributed memory machines. Now, however, there is growing evidence that it is possible to develop efficient and portable implementations of virtual shared memory models on scalable architectures. In this paper we discuss one particular example: Linda. After presenting an introduction to the Linda model, we focus on the expressiveness of the model, on techniques required to build efficient implementations, and on observed performance both on workstation networks and distributed-memory parallel machines. Finally, we conclude by briefly discussing the range of applications developed with Linda and Linda's suitability for the sorts of heterogeneous, dynamically-changing computational environments that are of growing significance.

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