We present an abstract interpretation technique to automatically build a Control Flow Graph (CFG) representation of the execution of a GPU kernel. GPUs implement an inherently parallel execution model, in which threads are grouped within so-called warps that execute in lockstep. This execution model enables the representation of the execution of the threads of a warp as a single CFG. However, thread divergence may appear within a warp and its effect must be captured explicitly within the CFG. Our method builds the CFG of a warp by applying abstract interpretation on the assembly (Nvidia SASS) code of a kernel, and by maintaining an abstract representation of which threads within the warp agree on which values. This allows the method to detect precisely the points in the program where thread divergence may occur, and avoid spurious reactivation edges in the CFG. We apply our technique on benchmark kernels as a proof-of-concept, and generate IPET systems using the resulting CFGs.