Understanding data reuse patterns of a computing system is crucial to effective design optimization. The emerging Single Instruction Multiple Threads (SIMT) processor adopts a programming model that is fundamentally disparate from conventional scalar processors. There is a lack of analytical approaches to quantify the data reuse of SIMT applications. This paper presents a quantitative method to study the data reuse inherent to SIMT applications. A metric, Data Reuse Degree, is defined to measure the amount of reused data between memory references, and associate each data reuse degree to a temporal distance representing the virtual time of the execution process. The experiments are performed on an abstracted SIMT processor that considers the programming model and runtime specifics. The experiments illustrate diverse data reuse patterns of SIMT applications and explore the impacts of architectural limitations.