Solving partial differential equations in a data-driven multiprocessor environment

The implementation of some partial differential equation (PDE) solvers (such as the Jacobi method) on a tagged token data-flow graph is demonstrated. Asynchronous methods (e.g. chaotic relaxation) are studied and other scheduling approaches (such as the token no-labeling scheme) are introduced to support the implementation of the asynchronous methods in a data-driven environment. High-level data-flow-language program constructs are introduced in order to handle chaotic operations. The performance of the program graphs is demonstrated by a deterministic simulation of a message-passing data-flow multiprocessor. An analysis of the overhead in the data-flow graphs is undertaken to demonstrate the limits of parallel operations in data-flow PDE program graphs