Computing models for functional language programs not only facilitate automatic exploitation of inherent parallelism, but they also provide for implicit tolerance to hardware faults through temporal and spatial redundancy. In this paper, we argue that fault tolerance can be achieved more efficiently by using intensional computing models (eduction) rather than extensional computing models (reduction). While intensional computing models can be implemented by using either data-driven execution or demand-driven execution, we show that the latter is naturally suited.

• ftcs21-preprint.ps