Reliable Scalable Symbolic Computation: The Design of SymGridPar2 Phil Trinder Symbolic computations are challenging to parallelise as they have complex data and control structures, and both dynamic and highly irregular parallelism. The SymGridPar framework has been developed to address these challenges on small-scale parallel architectures. However, as the number of cores in compute clusters continues to grow exponentially, and as the communication topology is becoming increasingly complex, an improved parallel symbolic computation framework is required: SymGridPar2. In this talk, I'll explain how two main aspects of the design of SymGridPar2, fault tolerance and locality control, interact with dynamic scheduling of parallelism. Fault tolerance is achieved by tracking the location of tasks as they are scheduled across the network, and by replicating tasks that were affected by node failure. Locality control exposes an abstraction of the communication topology so programs can control how close together tasks shall be placed by the dynamic scheduler.