The cuFFT library provides a simple interface for computing FFTs on an NVIDIA GPU, which allows users to quickly leverage the floating-point power and parallelism of the GPU in a highly optimized and tested FFT library. The cuFFT product supports a wide range of FFT inputs and options efficiently on NVIDIA GPUs. This decreases the latency versus the other methods. Recall that cuFFT is an optimized library for GPU. The Schönhage-Strassen method improves in performance with larger n since we prefer to use cuFFT-based multiplication for decomposed polynomials instead of schoolbook multiplication. 4.2 Experimental Results for the Signature Scheme. Performance¶ Here is the comparison to pure Cuda program using CUFFT. For Cuda test program see cuda folder in the distribution. Pyfft tests were executed with fast_math=True (default option for performance test script). In the following tables “sp” stands for “single precision”, “dp” for “double precision”.

In the cuFFT manual, it is explained that cuFFT uses two different algorithms for implementing the FFTs. One is the Cooley-Tuckey method and the other is the Bluestein algorithm. When the dimensions have prime factors of only 2,3,5 and 7 e.g (675 = 3^3 x 5^5), then 675 x 675 performs much much better than say 674 x 674 or 677 x 677. This document describes cuFFT, the NVIDIA® CUDA™ Fast Fourier Transform (FFT) product. It consists of two separate libraries: cuFFT and cuFFTW. The cuFFT library is designed to provide high performance on NVIDIA GPUs. The cuFFTW library is provided as a porting tool to enable users of FFTW to start using NVIDIA GPUs with a minimum amount of effort. cuFFT provides the ability to perform fast Fourier transforms (FFTs) on large datasets. Students will learn of common use cases such as fast multiplication of large polynomials, signal processing, and matrix operations. They will use this library to develop software that process audio or video signals. cuFFT Performance and Features Video 5:00..
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