GPU-FFT

December 19, 2025 · View on GitHub

Welcome to the GPU-FFT-Optimization repository! We present cutting-edge algorithms and implementations for optimizing the Fast Fourier Transform (FFT) on Graphics Processing Units (GPUs).

The associated research paper: https://eprint.iacr.org/2023/1410

NTT variant of GPU-FFT is available: https://github.com/Alisah-Ozcan/GPU-NTT

Development

Requirements

Build & Install

Configure + build:

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=86
cmake --build build --parallel

Install:

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=86
cmake --build build --parallel
cmake --install build

Notes:

  • If you install to a system location (default: /usr/local), you may need sudo or set -DCMAKE_INSTALL_PREFIX=/your/prefix.
  • If you omit -DCMAKE_CUDA_ARCHITECTURES=..., GPU-FFT defaults to 80;86;89;90.
  • If CMake cannot find nvcc, set CUDACXX=/path/to/nvcc or pass -DCMAKE_CUDA_COMPILER=/path/to/nvcc.
  • If you change compilers/toolchains, prefer a clean configure: cmake --fresh -S . -B build.

Compile Options

GPU-FFT uses C++17/CUDA17 and applies per-configuration compile flags.

  • Build type (single-config generators like Makefiles/Ninja): -DCMAKE_BUILD_TYPE=Release|Debug|RelWithDebInfo|MinSizeRel
  • Optimization/debug defaults:
    • Release: -O3 -DNDEBUG
    • RelWithDebInfo: -O3 -g -DNDEBUG
    • Debug: -g (and CUDA adds line info)
  • Extra warnings: -DGPUFFT_ENABLE_WARNINGS=ON

Example:

cmake -S . -B build -DCMAKE_BUILD_TYPE=RelWithDebInfo -DGPUFFT_ENABLE_WARNINGS=ON
cmake --build build --parallel

Testing & Benchmarking

CPU & GPU FFT Testing & Benchmarking

Choose one of data type which is upper line of the benchmark files:

  • typedef Float32 BenchmarkDataType;
  • typedef Float64 BenchmarkDataType;

To run examples:

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=86 -DGPUFFT_BUILD_EXAMPLES=ON
cmake --build build --parallel

./build/bin/example/cpu_fft_example <RING_SIZE_IN_LOG2> <BATCH_SIZE>
./build/bin/example/gpu_fft_C_C_example <RING_SIZE_IN_LOG2> <BATCH_SIZE>
./build/bin/example/gpu_fft_R_R_example <RING_SIZE_IN_LOG2> <BATCH_SIZE>
./build/bin/example/cpu_ffnt_example <RING_SIZE_IN_LOG2> <BATCH_SIZE>
./build/bin/example/gpu_ffnt_R_R_example <RING_SIZE_IN_LOG2> <BATCH_SIZE>
Example: ./build/bin/example/gpu_fft_R_R_example 12 1

To run benchmarks:

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=86 -DGPUFFT_BUILD_BENCHMARKS=ON
cmake --build build --parallel

./build/bin/benchmark/gpu_fft_C_C_mult_benchmark --disable-blocking-kernel
./build/bin/benchmark/gpu_fft_R_R_mult_benchmark --disable-blocking-kernel
./build/bin/benchmark/gpu_fft_benchmark --disable-blocking-kernel
./build/bin/benchmark/gpu_ffnt_R_R_mult_benchmark --disable-blocking-kernel

Using GPU-FFT in a downstream CMake project

Make sure GPU-FFT is installed before integrating it into your project. The installed GPU-FFT library provides a set of config files that make it easy to integrate GPU-FFT into your own CMake project. In your CMakeLists.txt, simply add:

project(<your-project> LANGUAGES CXX CUDA)
find_package(CUDAToolkit REQUIRED)
# ...
find_package(GPUFFT CONFIG REQUIRED)
# ...
target_link_libraries(<your-target> (PRIVATE|PUBLIC|INTERFACE) GPUFFT::fft CUDA::cudart)
# ...
set_target_properties(<your-target> PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
# ...

How to Cite GPU-FFT

Please use the below BibTeX, to cite GPU-FFT in academic papers.

@misc{cryptoeprint:2023/1410,
      author = {Ali Şah Özcan and Erkay Savaş},
      title = {Two Algorithms for Fast GPU Implementation of NTT},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1410},
      year = {2023},
      note = {\url{https://eprint.iacr.org/2023/1410}},
      url = {https://eprint.iacr.org/2023/1410}
}

License

This project is licensed under the Apache License. For more details, please refer to the License file.

Contact

If you have any questions or feedback, feel free to contact me: