A task graph is an intuitive way to represent the execution of parallel processes in many modern computing platforms. It can also be used for performance modeling and simulation in a network of computers. Common implementation of task graphs usually involves a form of message passing proto-col, which depends on a standard message passing library in the existing operating system. Not every emerging platform has such support from mainstream operating systems. For example the SpiNNaker system, which is a neuromorphic computer originally intended as a brain-style information processing system. As a massive many-core computing system, SpiNNaker not only offers abundant processing resources, but also a low-power and flexible application-oriented platform. In this paper we present an effi-cient mapping strategy for a task graph on a SpiNNaker machine. Our method relies on the existing low-level SpiNNaker’s kernel that provides the direct access to the SpiNNaker elements. As a result, a fault tolerant aware task graph framework suitable for high performance computing can be achieved. The experimental results show that SpiNNaker offers very low communication latency and demonstrate that our mapping strategy is suitable for large task graph networks.