Interpolation is one computational complex and time-consuming operation in the fields of spatial analysis that can not meet the real time demand. With the rapid increase of GPU floating-point computing power

general-purpose computation on graphics processors (GPGPU) has became an evolving research field in spatial information processing

and it provides an opportunity to accelerate some traditional inefficient algorithms. In this paper

we map the inverse distance weighted (IDW) interpolation method to the compute unified device architecture (CUDA) parallel programming model. Taking the advantage of graphics processing unit (GPU) parallel computing

we build two-level indexes on GPU

then blocking schemes are used to assign computing task among different threads. After illustrating the parallel interpolation process

we conduct several experiments

The experiment result shows that the error of this new method can control under 10 compared with CPU-based method. With larger influence radius and massive data

the performance can obtain above 40 times speedups over a very similar single-threaded CPU implementation. It is demonstrated the correctness and high efficiency of our optimized implementation.