  FFT (fast Fourier transform) plays a very important role in many fields, such
as digital signal processing, digital image processing and so on. However, in
application, FFT becomes a factor of affecting the processing efficiency,
especially in remote sensing, which large amounts of data need to be processed
with FFT. So shortening the FFT computation time is particularly important. GPU
(Graphics Processing Unit) has been used in many common areas and its
acceleration effect is very obvious compared with CPU (Central Processing Unit)
platform. In this paper, we present a new parallel method to execute FFT on
GPU. Based on GPU storage system and hardware processing pipeline, we improve
the way of data storage. We divided the data into parts reasonably according
the size of data to make full use of the characteristics of the GPU. We propose
the memory optimized method based on share memory and texture memory to reduce
the number of global memory access to achieve better efficiency. The results
show that the GPU-based memory optimized FFT implementation not only can
increase over 100% than FFTW library in CPU platform, but also can improve over
30% than CUFFT library in GPU platform.
