A from-scratch mixed-radix FFT library in C99 with hand-tuned AVX2/AVX-512 codelets.
Beats FFTW on 23 of 39 tested sizes. No external dependencies.

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Benchmark Results

Platform: Intel Core i9-14900KF · 48 KB L1d · DDR5 · AVX2 · FFTW 3.3.10 (FFTW_ESTIMATE)

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Accuracy

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Getting Started

git clone https://github.com/yourusername/VectorFFT.git
cd VectorFFT && mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release

#include "vfft_planner.h"
#include "vfft_register_codelets.h"

vfft_codelet_registry reg;
vfft_register_all(&reg);

vfft_plan *plan = vfft_plan_create(N, &reg);
vfft_execute_forward(plan, re, im, out_re, out_im);
vfft_plan_destroy(plan);

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Performance Tuning

VectorFFT uses measurement, not heuristics. Three calibration benchmarks determine every performance-critical threshold on your specific hardware:

./bench_walk          # Walk thresholds per radix    → vfft_calibration.txt
./bench_il            # IL crossovers per radix      → vfft_calibration.txt
./bench_factorize     # Optimal factorizations       → vfft_wisdom.txt

Both files are read automatically at plan creation. Without them, the planner uses conservative defaults — calibration typically improves performance by 15–40%.

Known limitations:

• DIF backward codelets are ~10–15% slower than DIT forward — primary source of roundtrip losses
• Small N (≤128) — per-stage overhead dominates; FFTW's monolithic codelets win here
• R=5 at K>2048 falls back to log3 derivation; planner minimizes this via R=10/R=25 fusion

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Acknowledgments

FFTW by Matteo Frigo and Steven G. Johnson — the gold standard. VectorFFT's prime codelets (R=17, 19, 23) are translated from FFTW's genfft output.

_{VectorFFT — Because every nanosecond counts.}