Accelerating InSAR raw data simulation on GPU using CUDA

This paper describes a scalable parallel method for interferometric synthetic aperture radar (InSAR) raw data simulation on graphic processing unit (GPU) with common unified device architecture (CUDA). The advantages of the new method rely on the three contributions: GPU hardware provides lots of stream processors for threads calculating, CUDA software environment runs thousands of threads working in parallel for assigned task, raw data simulation adopts the fine-grained task parallelism. Compared with OpenMP, MPI and grid computing, the method not only improves the computational efficiency greatly, but also save the resources such as hardware, electric power and room space. The results show that the method not only ensures accuracy, but also be able to obtain the speedup about 30 times.