Strategies for predictability in real-time data-flow architectures

Consideration is given to the development of strategies for predictable performance in homogeneous multicomputer data-flow architectures operating in real-time. Algorithms are restricted to the class of large-grained, decision-free algorithms. The mapping of such algorithms onto the specified class of data-flow architectures is realized by a new marked graph model called ATAMM (algorithm to architecture mapping model). Algorithm performance and resource needs are determined for predictable periodic execution of algorithms, which is achieved by algorithm modification and input data injection control. Performance is gracefully degraded to adapt to decreasing numbers of resources. The realization of the ATAMM model on a VHSIC four processor testbed is described. A software design tool for prediction of performance and resource requirements is described and is used to evaluate the performance of a space surveillance algorithm