Network Function Virtualization (NFV) enables network functions to run on general-purpose servers, thus alleviates the reliance on dedicated hardware and significantly improves the scalability and flexibility in networking service provisioning. Meanwhile, it is recognized that Virtualized Network Functions (VNFs) suffer from serious performance problem. Graphics Processing Unit (GPU), with massive processing cores, has been advocated as a potential accelerator for improving the performance efficiency of VNFs. However, the special architecture of GPU makes existing CPU-oriented task scheduling strategies fail to be applied, limiting the acceleration potential of GPUs. To this end, we propose a GPU-oriented spatio-temporal sharing framework as Gost to improve the performance of GPU-accelerated VNFs. We also study how to minimize the end-to-end latency of VNF flows via careful scheduling on the execution order and the GPU resource allocation (i.e., the number of threads). We first formally describe the problem as a non-linear integer programming problem, which is then equivalently transformed into an integer linear programming (ILP) form. Considering the high computation complexity of solving ILP, we further propose a customized list scheduling based spatio-temporal GPU sharing strategy (LSSTG). We have practically implemented a prototype of Gost, based on which we also verify the high efficiency of LSSTG by extensive experiments.