Constraint-based System Level Diagnosis Of Multiprocessor Architectures

In the latest years, new ideas appeared in system level diagnosis. Contrary to the traditional diagnosis models (like PMC, BGM etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self diagnosis, can be easily transformed to constraints between the state of the tester and the tested components, considering the test results. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The “benign” nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm’s complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. An experimental algorithm was implemented for a Parsytec GC tightly coupled multiprocessor system.