The limited execution speed of current full system simulators restricts their applicability for dynamic analysis to shortrunning workloads. When analyzing memory contents while simulating a kernel build with Simics, we encountered slowdowns of more than 5000x resulting in 10months of total simulation time. Prior work improved the simulation speed by simulating virtual CPU cores on separate physical CPU cores simultaneously or by applying sampling and extrapolation methods to focus costly analyses on short execution windows. However, these approaches inherently su er from limited scalability or trading accuracy for speed. SimuBoost is a novel idea to parallelize functional full system simulation of single-cores. Our approach takes advantage of fast execution through virtualization, taking checkpoints in regular intervals. The parts between subsequent checkpoints are then simulated and analyzed simultaneously in one job per interval. By transferring jobs to multiple nodes, a parallelized and distributed simulation of the target workload can be achieved, thereby e ectively reducing the overall required simulation time. As no implementation of SimuBoost exists yet, we present a formal model to evaluate the general speedup and scalability characteristics of our acceleration technique. We moreover provide a model to estimate the required number of simulation nodes for optimal performance. According to this model, our approach can speed up conventional simulation in a realistic scenario by a factor of 84, while delivering a parallelization efficiency of 94%
