Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
FFT and Convolution Performance in Image Filtering on GPU
IV '06 Proceedings of the conference on Information Visualization
A memory model for scientific algorithms on graphics processors
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Bandwidth intensive 3-D FFT kernel for GPUs using CUDA
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
GPU-based FFT computation for multi-gigabit wirelessHD baseband processing
EURASIP Journal on Wireless Communications and Networking
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Modern graphics processing units (GPU) are becoming more and more suitable for general purpose computing due to its growing computational power. These commodity processors follow, in general, a parallel SIMD execution model whose efficiency is subject to a right exploitation of the explicit memory hierarchy, among other factors. In this paper we analyze the implementation of the Fast Fourier Transform using the programming model of the Compute Unified Device Architecture (CUDA) recently released by NVIDIA for its new graphics platforms. Within this model we propose an FFT implementation that takes into account memory reference locality issues that are crucial in order to achieve a high execution performance. This proposal has been experimentally tested and compared with other well known approaches such as the manufacturer's FFT library.