Two tools for interprocess communication in distributed data-flow systems

Existing data-flow languages are incapable of supporting large, manageable and reliable non-deterministic programs. Operating system implementations and real-time applications require high-level non-deterministic programming facilities. Hoare's Communicating Sequential Processes and Brinch Hansen's Distributed Processes are two widely acclaimed distributed programming languages for conventional distributed computing systems. The powerful constructs of interprocess communication and synchronisation supported by the above two languages have been incorporated in Communicating Data Flow Channels (CDFC), and Distributed Data Flow Channels (DDFC), two languages proposed in this paper for distributed processing in data-flow systems. Several new features have been incorporated in these languages to make their execution more efficient in a data-flow environment. The implementation of these two languages on an abstract data-flow machine is investigated in detail. Two simulators have been implemented for testing the effectiveness of our implementation schemes for these two data-flow languages. These simulators are written in Pascal and run on a DEC 1090 computing system. This work demonstrates that a data-flow computing system can accommodate, without taking resort to specialised hardware units, the non-deterministic and synchronisation constructs of conventional languages with the attendant benefits of parallel data-driven execution