| Summary: | The shared data-object model is designed to ease the implementation of parallel applications on loosely coupled distributed systems. Unlike most other models for distributed programming (e.g., RPC), the shared data-object model allows processes on different machines to share data. Such data are encapsulated in data-objects, which are instances of user-defined abstract data types. The shared data-object model forms the basis of a new language for distributed programming, Orca, which gives linguistic support for parallelism and data-objects. A distributed implementation of the shared data-object model should take care of the physical distribution of objects among the local memories of the processors. In particular, an implementation may replicate objects in order to decrease access times to objects and increase parallelism. The intent of this paper is to show that, for several applications, the proposed model is both easy to use and efficient. We first give a brief description of the sha.
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