Constraint-based Diagnosis Algorithms For Multiprocessors

In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In 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 into constraints between the state of the tester and the tested components. 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, havea 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. Experimental algorithms #using both centralized and distributed approach# were implemented foraParsytec GC massively parallel multiprocessor system.