Most of the existing packet-level simulation tools are designed to perform experiments modeling small to medium scale networks. The main reason of this limitation is the
amount of available computation power and memory in CPUbased simulation environments. To enable efficient packet-level simulation for large scale scenarios, we introduce a CPU-GPU co-simulation framework where synchronization and experiment design are performed in CPU and node’s logical processes are executed in parallel in GPU according to the master/worker model. The framework is developed using the Compute-Unified
Device Architecture (CUDA) API and denoted as Cunetsim, CUDA network simulator. In this work, we study the node mobility and connectivity as they are among the most time consuming task when large scale networks are simulated. Simulation results show that Cunetsim runtime remains stable and that it achieves significantly lower runtime than existing approaches when computing mobility and connectivity with no degradation in the accuracy of the results. Further, the connectivity is achieved up to 870 times faster than Sinalgo, which presents the best performances know until now.
