Three non conventional paradigms of parallel computation

We consider three paradigms of computation where the benefits of a parallel solution are greater than usual. Paradigm 1 works on a timevarying input data set, whose size increases with time. In paradigm 2 the data set is fixed, but the processors may fail at any time with a given constant probability. In paradigm 3, the execution of a single operation may require more than one processor, for security or reliability reasons. We discuss the organization of PRAM algorithms for these paradigms, and prove new bounds on parallel speed-up.