More about FFT/DFT

The Fast Fourier Transform (FFT) can be used to perform:

• Convolution (including convolution reverberation)
• Cross-correlation and auto-correlation
• Applying large FIR filters
• Sample rate conversion
• Spectrum visualization
• Large integer multiplication
• Wavelet transform
• and many other algorithms

Often, FFT is the most efficient way to perform each of these algorithms.

About KFR DFT implementation

The KFR implementation of the FFT:

• is fully optimized for X86, X86-64, ARM and AARCH64 (ARM64) processors
• uses vector intrinsics (if available for the cpu)
• supports both single- and double-precision
• supports in-place processing
• supports multidimensional FFT
• can cache internal data between calls to speed up plan creation
• can do forward and inverse FFT without the need to create two plans
• can be used for complex-to-complex, real-to-complex and complex-to-real transforms
• doesn’t require measuring FFT performance at runtime to find an optimal configuration
• has special implementations for FFT sizes up to 1024
• has no external dependencies
• is thread-safe, with no global data
• is written in modern C++17
• is open source (GPL v2+ license, commercial license is availalble for closed source projects, see https://kfr.dev )