6 Commits

Author SHA1 Message Date
Bram Veenboer
2a10cad3dd Fix compilation errors 2025-09-02 16:59:19 +02:00
Bram Veenboer
2c2a59d6d6 Apply formatting 2025-09-02 16:26:59 +02:00
Bram Veenboer
a1f2dd6c4d Apply suggestions from code review
Co-authored-by: Wiebe van Breukelen <breukelen@astron.nl>
2025-09-02 16:26:27 +02:00
Bram Veenboer
3dcca92b79 Remove remaining init and std::memcpy 2025-09-02 13:19:50 +02:00
Bram Veenboer
8df4bbf54e Add allocate_memory and free_memory 2025-09-02 12:03:31 +02:00
Bram Veenboer
716f323b26 Update GPU memory management
- Move device memory allocation for output out of init
- Copy directly from device memory to host pointers
2025-09-02 09:33:36 +02:00
12 changed files with 28 additions and 142 deletions

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@@ -8,4 +8,3 @@ repos:
hooks:
- id: cmake-format
- id: cmake-lint
args: [--disabled-codes=C0301]

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@@ -12,11 +12,6 @@ option(TRIGDX_BUILD_TESTS "Build tests" ON)
option(TRIGDX_BUILD_BENCHMARKS "Build tests" ON)
option(TRIGDX_BUILD_PYTHON "Build Python interface" ON)
# Add compiler flags
set(CMAKE_CXX_FLAGS
"${CMAKE_CXX_FLAGS} -Wall -Wnon-virtual-dtor -Wduplicated-branches -Wvla -Wpointer-arith -Wextra -Wno-unused-parameter"
)
list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
configure_file(
${CMAKE_CURRENT_SOURCE_DIR}/cmake/trigdx_config.hpp.in

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@@ -1,54 +0,0 @@
# 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.

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@@ -26,9 +26,6 @@ static void benchmark_sinf(benchmark::State &state) {
reinterpret_cast<float *>(backend.allocate_memory(N * sizeof(float)));
float *s =
reinterpret_cast<float *>(backend.allocate_memory(N * sizeof(float)));
if (!x || !s) {
throw std::runtime_error("Buffer allocation failed");
}
auto end = std::chrono::high_resolution_clock::now();
state.counters["init_ms"] =
std::chrono::duration_cast<std::chrono::microseconds>(end - start)

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@@ -16,7 +16,7 @@ public:
return static_cast<void *>(new uint8_t[bytes]);
};
virtual void free_memory(void *ptr) const { delete[] static_cast<uint8_t*>(ptr); };
virtual void free_memory(void *ptr) const { std::free(ptr); };
// Compute sine for n elements
virtual void compute_sinf(size_t n, const float *x, float *s) const = 0;

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@@ -8,16 +8,5 @@ if(NOT pybind11_FOUND)
FetchContent_MakeAvailable(pybind11)
endif()
# Needed to set ${Python_VERSION_MAJOR} and ${Python_VERSION_MINOR}
find_package(Python REQUIRED)
pybind11_add_module(pytrigdx bindings.cpp)
target_link_libraries(pytrigdx PRIVATE trigdx)
set_target_properties(pytrigdx PROPERTIES OUTPUT_NAME "trigdx")
set(PYTHON_SITE_PACKAGES
"${CMAKE_INSTALL_LIBDIR}/python${Python_VERSION_MAJOR}.${Python_VERSION_MINOR}/site-packages/trigdx"
)
install(TARGETS pytrigdx DESTINATION ${PYTHON_SITE_PACKAGES})
install(FILES __init__.py DESTINATION ${PYTHON_SITE_PACKAGES})

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@@ -1,16 +0,0 @@
from .trigdx import Reference, Lookup16K, Lookup32K, LookupAVX16K, LookupAVX32K
try:
from .trigdx import MKL
except ImportError:
pass
try:
from .trigdx import GPU
except ImportError:
pass
try:
from .trigdx import LookupXSIMD16K, LookupXSIMD32K
except ImportError:
pass

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@@ -72,9 +72,7 @@ void bind_backend(py::module &m, const char *name) {
.def("compute_sincosf", &compute_sincos<float>);
}
PYBIND11_MODULE(trigdx, m) {
m.doc() = "TrigDx python bindings";
PYBIND11_MODULE(pytrigdx, m) {
py::class_<Backend, std::shared_ptr<Backend>>(m, "Backend")
.def("init", &Backend::init);

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@@ -2,24 +2,6 @@ include(FetchContent)
include(FindAVX)
add_library(trigdx reference.cpp lookup.cpp)
if(HAVE_AVX2)
target_compile_definitions(trigdx PUBLIC HAVE_AVX2)
if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel" OR CMAKE_CXX_COMPILER_ID STREQUAL
"IntelLLVM")
target_compile_options(trigdx PUBLIC -xCORE-AVX2)
else()
target_compile_options(trigdx PUBLIC -mavx2)
endif()
elseif(HAVE_AVX)
target_compile_definitions(trigdx PUBLIC HAVE_AVX)
if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel" OR CMAKE_CXX_COMPILER_ID STREQUAL
"IntelLLVM")
target_compile_options(trigdx PUBLIC -xAVX)
else()
target_compile_options(trigdx PUBLIC -mavx)
endif()
endif()
target_include_directories(trigdx PUBLIC ${PROJECT_SOURCE_DIR}/include)
if(HAVE_AVX)

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@@ -6,16 +6,6 @@
#include "trigdx/lookup_avx.hpp"
#if defined(HAVE_AVX) && !defined(__AVX__)
static_assert(HAVE_AVX == 0, "__AVX__ should be defined when HAVE_AVX is "
"defined");
#endif
#if defined(HAVE_AVX2) && !defined(__AVX2__)
static_assert(HAVE_AVX2 == 0, "__AVX2__ should be defined when HAVE_AVX2 is "
"defined");
#endif
template <std::size_t NR_SAMPLES> struct LookupAVXBackend<NR_SAMPLES>::Impl {
std::vector<float> lookup;
static constexpr std::size_t MASK = NR_SAMPLES - 1;
@@ -89,6 +79,7 @@ template <std::size_t NR_SAMPLES> struct LookupAVXBackend<NR_SAMPLES>::Impl {
constexpr std::size_t VL = 8; // AVX processes 8 floats
const __m256 scale = _mm256_set1_ps(SCALE);
const __m256i mask = _mm256_set1_epi32(MASK);
const __m256i quarter_pi = _mm256_set1_epi32(NR_SAMPLES / 4);
std::size_t i = 0;
for (; i + VL <= n; i += VL) {
@@ -103,7 +94,7 @@ template <std::size_t NR_SAMPLES> struct LookupAVXBackend<NR_SAMPLES>::Impl {
#else
// fallback gather for AVX1
float sin_tmp[VL];
int idx_a[VL];
int idx_a[VL], idxc_a[VL];
_mm256_store_si256((__m256i *)idx_a, idx);
for (std::size_t k = 0; k < VL; ++k) {
sin_tmp[k] = lookup[idx_a[k]];

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@@ -20,8 +20,8 @@ template <std::size_t NR_SAMPLES> struct lookup_table {
cos_values[i] = cosf(i * PI_FRAC);
}
}
std::array<float, NR_SAMPLES> sin_values;
std::array<float, NR_SAMPLES> cos_values;
std::array<float, NR_SAMPLES> sin_values;
};
template <std::size_t NR_SAMPLES> struct cosf_dispatcher {
@@ -33,6 +33,7 @@ template <std::size_t NR_SAMPLES> struct cosf_dispatcher {
constexpr uint_fast32_t VL = b_type::size;
const uint_fast32_t VS = n - n % VL;
const uint_fast32_t Q_PI = NR_SAMPLES / 4U;
const b_type scale = b_type::broadcast(lookup_table_.SCALE);
const b_type pi_frac = b_type::broadcast(lookup_table_.PI_FRAC);
const m_type mask = m_type::broadcast(lookup_table_.MASK);
@@ -41,7 +42,7 @@ template <std::size_t NR_SAMPLES> struct cosf_dispatcher {
const b_type term2 = b_type::broadcast(lookup_table_.TERM2); // 1/2!
const b_type term3 = b_type::broadcast(lookup_table_.TERM3); // 1/3!
const b_type term4 = b_type::broadcast(lookup_table_.TERM4); // 1/4!
const m_type quarter_pi = m_type::broadcast(Q_PI);
uint_fast32_t i;
for (i = 0; i < VS; i += VL) {
const b_type vx = b_type::load(a + i, Tag());
@@ -56,10 +57,10 @@ template <std::size_t NR_SAMPLES> struct cosf_dispatcher {
const b_type dx = xsimd::sub(vx, xsimd::mul(f_idx, pi_frac));
const b_type dx2 = xsimd::mul(dx, dx);
const b_type dx3 = xsimd::mul(dx2, dx);
const b_type dx4 = xsimd::mul(dx2, dx2);
const b_type dx4 = xsimd::mul(dx2, dx);
const b_type t2 = xsimd::mul(dx2, term2);
const b_type t3 = xsimd::mul(dx3, term3);
const b_type t4 = xsimd::mul(dx4, term4);
const b_type t4 = xsimd::mul(dx4, term3);
const b_type cosdx = xsimd::add(xsimd::sub(term1, t2), t4);
@@ -78,7 +79,7 @@ template <std::size_t NR_SAMPLES> struct cosf_dispatcher {
const float dx = a[i] - idx * lookup_table_.PI_FRAC;
const float dx2 = dx * dx;
const float dx3 = dx2 * dx;
const float dx4 = dx2 * dx2;
const float dx4 = dx3 * dx;
const float cosdx =
1.0f - lookup_table_.TERM2 * dx2 + lookup_table_.TERM4 * dx4;
const float sindx = dx - lookup_table_.TERM3 * dx3;
@@ -97,6 +98,7 @@ template <std::size_t NR_SAMPLES> struct sinf_dispatcher {
constexpr uint_fast32_t VL = b_type::size;
const uint_fast32_t VS = n - n % VL;
const uint_fast32_t Q_PI = NR_SAMPLES / 4U;
const b_type scale = b_type::broadcast(lookup_table_.SCALE);
const b_type pi_frac = b_type::broadcast(lookup_table_.PI_FRAC);
const m_type mask = m_type::broadcast(lookup_table_.MASK);
@@ -105,7 +107,7 @@ template <std::size_t NR_SAMPLES> struct sinf_dispatcher {
const b_type term2 = b_type::broadcast(lookup_table_.TERM2); // 1/2!
const b_type term3 = b_type::broadcast(lookup_table_.TERM3); // 1/3!
const b_type term4 = b_type::broadcast(lookup_table_.TERM4); // 1/4!
const m_type quarter_pi = m_type::broadcast(Q_PI);
uint_fast32_t i;
for (i = 0; i < VS; i += VL) {
const b_type vx = b_type::load(a + i, Tag());
@@ -115,10 +117,10 @@ template <std::size_t NR_SAMPLES> struct sinf_dispatcher {
const b_type dx = xsimd::sub(vx, xsimd::mul(f_idx, pi_frac));
const b_type dx2 = xsimd::mul(dx, dx);
const b_type dx3 = xsimd::mul(dx2, dx);
const b_type dx4 = xsimd::mul(dx2, dx2);
const b_type dx4 = xsimd::mul(dx2, dx);
const b_type t2 = xsimd::mul(dx2, term2);
const b_type t3 = xsimd::mul(dx3, term3);
const b_type t4 = xsimd::mul(dx4, term4);
const b_type t4 = xsimd::mul(dx4, term3);
const b_type cosdx = xsimd::add(xsimd::sub(term1, t2), t4);
const b_type sindx = xsimd::sub(dx, t3);
@@ -138,7 +140,7 @@ template <std::size_t NR_SAMPLES> struct sinf_dispatcher {
const float dx = a[i] - idx * lookup_table_.PI_FRAC;
const float dx2 = dx * dx;
const float dx3 = dx2 * dx;
const float dx4 = dx2 * dx2;
const float dx4 = dx3 * dx;
const float cosdx =
1.0f - lookup_table_.TERM2 * dx2 + lookup_table_.TERM4 * dx4;
const float sindx = dx - lookup_table_.TERM3 * dx3;
@@ -158,6 +160,7 @@ template <std::size_t NR_SAMPLES> struct sin_cosf_dispatcher {
constexpr uint_fast32_t VL = b_type::size;
const uint_fast32_t VS = n - n % VL;
const uint_fast32_t Q_PI = NR_SAMPLES / 4U;
const b_type scale = b_type::broadcast(lookup_table_.SCALE);
const m_type mask = m_type::broadcast(lookup_table_.MASK);
const b_type pi_frac = b_type::broadcast(lookup_table_.PI_FRAC);
@@ -167,6 +170,7 @@ template <std::size_t NR_SAMPLES> struct sin_cosf_dispatcher {
const b_type term3 = b_type::broadcast(lookup_table_.TERM3); // 1/3!
const b_type term4 = b_type::broadcast(lookup_table_.TERM4); // 1/4!
const m_type quarter_pi = m_type::broadcast(Q_PI);
uint_fast32_t i;
for (i = 0; i < VS; i += VL) {
const b_type vx = b_type::load(a + i, Tag());
@@ -176,20 +180,20 @@ template <std::size_t NR_SAMPLES> struct sin_cosf_dispatcher {
const b_type dx = xsimd::sub(vx, xsimd::mul(f_idx, pi_frac));
const b_type dx2 = xsimd::mul(dx, dx);
const b_type dx3 = xsimd::mul(dx2, dx);
const b_type dx4 = xsimd::mul(dx2, dx2);
const b_type dx4 = xsimd::mul(dx2, dx);
const b_type t2 = xsimd::mul(dx2, term2);
const b_type t3 = xsimd::mul(dx3, term3);
const b_type t4 = xsimd::mul(dx4, term4);
const b_type t4 = xsimd::mul(dx4, term3);
idx = xsimd::bitwise_and(idx, mask);
const b_type sinv_base = b_type::gather(lookup_table_.sin_values.data(), idx);
const b_type cosv_base = b_type::gather(lookup_table_.cos_values.data(), idx);
b_type sinv = b_type::gather(lookup_table_.sin_values.data(), idx);
b_type cosv = b_type::gather(lookup_table_.cos_values.data(), idx);
const b_type cosdx = xsimd::add(xsimd::sub(term1, t2), t4);
const b_type sindx = xsimd::sub(dx, t3);
b_type sinv = xsimd::add(xsimd::mul(cosv_base, sindx), xsimd::mul(sinv_base, cosdx));
b_type cosv = xsimd::sub(xsimd::mul(cosv_base, cosdx), xsimd::mul(sinv_base, sindx));
sinv = xsimd::add(xsimd::mul(cosv, sindx), xsimd::mul(sinv, cosdx));
cosv = xsimd::sub(xsimd::mul(cosv, cosdx), xsimd::mul(sinv, sindx));
sinv.store(s + i, Tag());
cosv.store(c + i, Tag());
@@ -202,7 +206,7 @@ template <std::size_t NR_SAMPLES> struct sin_cosf_dispatcher {
const float dx = a[i] - idx * lookup_table_.PI_FRAC;
const float dx2 = dx * dx;
const float dx3 = dx2 * dx;
const float dx4 = dx2 * dx2;
const float dx4 = dx3 * dx;
const float cosdx =
1.0f - lookup_table_.TERM2 * dx2 + lookup_table_.TERM4 * dx4;
const float sindx = dx - lookup_table_.TERM3 * dx3;

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@@ -17,6 +17,7 @@ void ReferenceBackend::compute_cosf(size_t n, const float *x, float *c) const {
void ReferenceBackend::compute_sincosf(size_t n, const float *x, float *s,
float *c) const {
for (size_t i = 0; i < n; ++i) {
sincosf(x[i], &s[i], &c[i]);
s[i] = sinf(x[i]);
c[i] = cosf(x[i]);
}
}