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The Fast Fourier Transform is fundamental to signal processing, spectral analysis, and can accelerate large convolutions. NVIDIA's cuFFT library provides highly optimized FFT implementations, but proper usage requires understanding plans, memory layouts, and batching. This guide covers cuFFT best practices, memory optimization, and when FFT-based convolution outperforms direct methods.
Create plans once, reuse for same-size transforms.
Process multiple signals with single plan for better GPU utilization.
Use same buffer for input/output to halve memory.
Creating/destroying plans per call adds significant overhead.
#include <cufft.h>
void fft_naive(cufftComplex* d_data, int N) {
cufftHandle plan;
// Plan created every call - expensive!
cufftPlan1d(&plan, N, CUFFT_C2C, 1);
// Execute forward FFT
cufftExecC2C(plan, d_data, d_data, CUFFT_FORWARD);
// Destroy plan every call - wasteful
cufftDestroy(plan);
}Reusing plans and batching eliminates overhead.
class FFTProcessor {
cufftHandle plan;
int n, batch;
public:
FFTProcessor(int n, int batch) : n(n), batch(batch) {
// Create plan once
cufftPlan1d(&plan, n, CUFFT_C2C, batch);
// Optional: use custom work area for memory control
size_t workSize;
cufftGetSize(plan, &workSize);
cufftSetAutoAllocation(plan, 0); // Manual memory
}
void forward(cufftComplex* data) {
cufftExecC2C(plan, data, data, CUFFT_FORWARD); // In-place
}
void inverse(cufftComplex* data) {
cufftExecC2C(plan, data, data, CUFFT_INVERSE);
// Note: cuFFT doesn't normalize - divide by N
}
~FFTProcessor() {
cufftDestroy(plan);
}
};
// For 2D batched FFT (e.g., image processing):
cufftHandle plan2d;
int dims[2] = {height, width};
cufftPlanMany(&plan2d, 2, dims,
NULL, 1, height*width, // input strides
NULL, 1, height*width, // output strides
CUFFT_C2C, batch);| Metric | Naive | Optimized | Improvement |
|---|---|---|---|
| Plan reuse speedup | 1x | 10-100x | For small N |
| Batched vs loop | 1x | 3-5x | Better GPU utilization |
FFT convolution is O(N log N) vs O(N*K) for direct. It wins when kernel size K > ~100. For small kernels, direct convolution with cuDNN is faster.
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