Distributed input/output processing in data-driven multiprocessors

Data-flow principles of execution provide an elegant way to ensure at runtime that instructions can be executed asynchronously in a parallel environment. However, while the conventional von Neumann model of interpretation has a very rigid ordering of instructions, it is the very asynchronous character of the data-flow model of execution that introduces conflicts when `state´ tasks (such as I/O operations) must share common data objects. In order to execute I/O operations safely and in parallel, an algorithm to detect and classify cases of potential conflicts (hazards) has been developed and is described. It is based upon localizing the effect of I/O operations by splitting the data-flow graph into two subgraphs: (a) the computation subgraph, and (b) the I/O subgraph. The scheme presented thus enables the creation and interaction of both subgraphs, which in turn yields a deterministic execution. Furthermore, the proposed scheme enables the distributed execution of I/O operations as permitted by data dependencies