An Efficient Linear Language for Parallel Symbolic Processing

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Next-generation intelligent systems and robots will require the power of parallel computer, yet most novel parallel languages are geared primarily for numerical calculation--e.g., signal processing--in which the data values are elementary numerical samples. These models of computation fail miserably for calculations involving the highly-structured symbolic data required for artificial intelligence application. A "linear" language provides an elegant and intuitive "mechanical" model of parallel computation in which structured values are transmitted as simply as elementary values. These "linear" data structures retain the ability to transmit highly structured information, yet avoid the problems of aliasing and sharing. The efficiency of existing linear language promising new implementation techniques. In Phase I, we will investigate the ability of our novel implementation approach to provide most of the efficiency of nonlinear shared data structures while preserving the elegant linear data model. If successful, our Phase II implementation of a parallel linear language will provide efficient execution, together with an elegant and intuitive programming model. This combination should increase the productivity of software development for intelligent applications utilizing MIMD parallel computers.//