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trigdx/README.md
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Co-authored-by: Mick Veldhuis <mickveldhuis@hotmail.nl>

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Co-authored-by: Mick Veldhuis <mickveldhuis@hotmail.nl>
2025-10-22 16:55:37 +02:00

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# TrigDx
Highperformance C++ library offering multiple implementations of transcendental trigonometric functions (e.g., sin, cos, tan and their variants), designed for numerical, signalprocessing, and realtime systems where trading a small loss of accuracy for significantly higher throughput on modern CPUs (scalar and SIMD) and NVIDIA GPUs is acceptable.
## Why TrigDx?
Many applications use the standard library implementations, which prioritise correctness but are not always optimal for throughput on vectorized or GPU hardware. TrigDx gives you multiple implementations so you can:
- Replace `std::sin` / `std::cos` calls with faster approximations when a small, bounded reduction in accuracy is acceptable.
- Use SIMD/vectorized implementations and compact lookup tables for high throughput lookups.
- Run massively parallel kernels that take advantage of a GPU's _Special Function Units_ (SFUs).
## Requirements
- A C++ compiler with at least C++17 support (GCC, Clang)
- CMake 3.15+
- Optional: NVIDIA CUDA Toolkit 11+ to build GPU kernels
- Optional: GoogleTest (for unit tests) and GoogleBenchmark (for microbenchmarks)
## Building
```bash
git clone https://github.com/astron-rd/TrigDx.git
cd TrigDx
mkdir build && cd build
# CPU-only:
cmake -DCMAKE_BUILD_TYPE=Release -DTRIGDX_USE_XSIMD=ON ..
cmake --build . -j
# Enable CUDA (if available):
cmake -DCMAKE_BUILD_TYPE=Release -DTRIGDX_USE_GPU=ON ..
cmake --build . -j
# Run tests:
ctest --output-on-failure -j
```
Common CMake options:
- `TRIGDX_USE_GPU=ON/OFF` — build GPU support.
- `TRIGDX_BUILD_TESTS=ON/OFF` — build tests.
- `TRIGDX_BUILD_BENCHMARKS=ON/OFF` — build benchmarks.
- `TRIGDX_BUILD_PYTHON` — build Python interface.
## Contributing
- Fork → create a feature branch → open a PR.
- Include unit tests for correctnesssensitive changes and benchmark results for performance changes.
- Follow project style (clangformat) and run tests locally before submitting.
## Reporting issues
When opening an issue for incorrect results or performance regressions, please include:
- Platform and CPU/GPU model.
- Compiler and version with exact compile flags.
- Small reproducer (input data and the TrigDx implementation used).
## License
See the LICENSE file in the repository for licensing details.