After years of research, static program analysis has made great progress in many aspects. However, performing sophisticated program analysis over large-scale software systems (such as Linux kernel, Hadoop, etc.) is still challenging due to its high complexity. To address the poor scalability of static analysis, with the rise of multi-core computation architectures, researchers have proposed various parallel static analysis techniques. This paper first introduces the basic concepts of static analysis, the key techniques, and the challenges. Then the traditional optimization approaches are discussed followed by the studies in parallelizing static analysis in three categories—CPU parallelism, distributed and GPU implementation, and the representative parallel static analysis tools. Finally, the potential research trend in parallelizing static analysis is described.