https://github.com/xtensor-stack/xsimd
C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
https://github.com/xtensor-stack/xsimd
avx avx512 c-plus-plus-11 cpp mathematical-functions neon simd simd-instructions simd-intrinsics sse sve vectorization
Last synced: 6 months ago
JSON representation
C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
- Host: GitHub
- URL: https://github.com/xtensor-stack/xsimd
- Owner: xtensor-stack
- License: bsd-3-clause
- Created: 2016-02-19T22:41:39.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2025-04-27T14:35:43.000Z (11 months ago)
- Last Synced: 2025-04-27T15:31:20.961Z (11 months ago)
- Topics: avx, avx512, c-plus-plus-11, cpp, mathematical-functions, neon, simd, simd-instructions, simd-intrinsics, sse, sve, vectorization
- Language: C++
- Homepage: https://xsimd.readthedocs.io/
- Size: 3.81 MB
- Stars: 2,358
- Watchers: 75
- Forks: 265
- Open Issues: 47
-
Metadata Files:
- Readme: README.md
- Changelog: Changelog.rst
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- AwesomeCppGameDev - xsimd
- StarryDivineSky - xtensor-stack/xsimd - stack/xsimd 是一个专注于C++语言的SIMD（单指令多数据）指令集封装库，旨在为开发者提供跨平台、高性能的向量化计算能力。该项目通过封装多种硬件架构的SIMD指令（如SSE、AVX、AVX512、NEON、SVE、WebAssembly、VSX、RISC-V等），为数学运算、数据处理和科学计算提供优化后的并行化函数实现，显著提升计算密集型任务的效率。其核心工作原理是通过C++头文件库的形式，自动适配不同CPU架构的SIMD指令集，无需手动编写汇编代码，开发者只需调用预定义的函数即可触发底层硬件加速。 项目特别强调跨平台兼容性，支持从x86到ARM架构的主流硬件，同时覆盖WebAssembly等新兴平台，确保代码在不同环境中保持性能一致性。其设计注重易用性，提供与xsimd库的无缝集成，允许用户通过CMake配置自动选择目标平台的指令集，并支持运行时检测硬件特性以动态调整计算方式。性能优势体现在对向量化计算（如向量加法、乘法、归约操作）和并行化算法的深度优化，尤其适合处理大规模数值计算、机器学习、图像处理等场景。 xsimd的项目结构采用模块化设计，核心功能通过头文件实现，减少编译依赖，同时提供详细的文档和示例代码。开发者可通过GitHub社区获取支持，项目持续维护并适配新硬件架构。总之，该项目为需要高性能计算的C++开发者提供了统一的SIMD编程接口，降低了底层硬件优化的复杂度，同时兼顾灵活性和跨平台能力。 (CPU_RISC-V / 资源传输下载)
README
# 
[](https://github.com/xtensor-stack/xsimd/actions/workflows/android.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/cross-rvv.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/cross-sve.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/cross.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/cxx-no-exceptions.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/cxx-versions.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/emscripten.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/linux.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/macos.yml)
[](https://github.com/xtensor-stack/xsimd/actions/workflows/windows.yml)
[](https://xsimd.readthedocs.io/en/latest/?badge=latest)
[](https://gitter.im/QuantStack/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
C++ wrappers for SIMD intrinsics
## Introduction
SIMD (Single Instruction, Multiple Data) is a feature of microprocessors that has been available for many years. SIMD instructions perform a single operation
on a batch of values at once, and thus provide a way to significantly accelerate code execution. However, these instructions differ between microprocessor
vendors and compilers.
`xsimd` provides a unified means for using these features for library authors. Namely, it enables manipulation of batches of numbers with the same arithmetic operators as for single values. It also provides accelerated implementation of common mathematical functions operating on batches.
## Adoption
Beyond Xtensor, Xsimd has been adopted by major open-source projects, such as Mozilla Firefox, Apache Arrow, Pythran, and Krita.
## History
The XSimd project started with a series of blog articles by Johan Mabille on how to implement wrappers for SIMD intrinsicts.
The archives of the blog can be found here: [The C++ Scientist](http://johanmabille.github.io/blog/archives/). The design described in
the articles remained close to the actual architecture of XSimd up until Version 8.0.
The mathematical functions are a lightweight implementation of the algorithms originally implemented in the now deprecated [boost.SIMD](https://github.com/NumScale/boost.simd) project.
## Requirements
`xsimd` requires a C++11 compliant compiler. The following C++ compilers are supported:
Compiler | Version
------------------------|-------------------------------
Microsoft Visual Studio | MSVC 2015 update 2 and above
g++ | 4.9 and above
clang | 4.0 and above
The following SIMD instruction set extensions are supported:
Architecture | Instruction set extensions
-------------|-----------------------------------------------------
x86 | SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2, FMA3+SSE, FMA3+AVX, FMA3+AVX2
x86 | AVX512BW, AVX512CD, AVX512DQ, AVX512F (gcc7 and higher)
x86 AMD | FMA4
ARM | NEON, NEON64, SVE128/256/512 (fixed vector size)
WebAssembly | WASM
powerpc64 | VSX
RISC-V | RISC-V128/256/512 (fixed vector size)
## Installation
### Install from conda-forge
A package for xsimd is available on the mamba (or conda) package manager.
```bash
mamba install -c conda-forge xsimd
```
### Install with Spack
A package for xsimd is available on the Spack package manager.
```bash
spack install xsimd
spack load xsimd
```
### Install from sources
You can directly install it from the sources with cmake:
```bash
cmake -D CMAKE_INSTALL_PREFIX=your_install_prefix .
make install
```
## Documentation
To get started with using `xsimd`, check out the full documentation
http://xsimd.readthedocs.io/
## Dependencies
`xsimd` has an optional dependency on the [xtl](https://github.com/xtensor-stack/xtl) library:
| `xsimd` | `xtl` (optional) |
|---------|------------------|
| master | ^0.7.0 |
| 12.x | ^0.7.0 |
| 11.x | ^0.7.0 |
| 10.x | ^0.7.0 |
| 9.x | ^0.7.0 |
| 8.x | ^0.7.0 |
The dependency on `xtl` is required if you want to support vectorization for `xtl::xcomplex`. In this case, you must build your project with C++14 support enabled.
## Usage
The version 8 of the library is a complete rewrite and there are some slight differences with 7.x versions.
A migration guide will be available soon. In the meanwhile, the following examples show how to use both versions
7 and 8 of the library?
### Explicit use of an instruction set extension
Here is an example that computes the mean of two sets of 4 double floating point values, assuming AVX extension is supported:
```cpp
#include
#include "xsimd/xsimd.hpp"
namespace xs = xsimd;
int main(int argc, char* argv[])
{
xs::batch a = {1.5, 2.5, 3.5, 4.5};
xs::batch b = {2.5, 3.5, 4.5, 5.5};
auto mean = (a + b) / 2;
std::cout << mean << std::endl;
return 0;
}
```
Do not forget to enable AVX extension when building the example. With gcc or clang, this is done with the `-mavx` flag,
on MSVC you have to pass the `/arch:AVX` option.
This example outputs:
```cpp
(2.0, 3.0, 4.0, 5.0)
```
### Auto detection of the instruction set extension to be used
The same computation operating on vectors and using the most performant instruction set available:
```cpp
#include
#include
#include "xsimd/xsimd.hpp"
namespace xs = xsimd;
using vector_type = std::vector>;
void mean(const vector_type& a, const vector_type& b, vector_type& res)
{
std::size_t size = a.size();
constexpr std::size_t simd_size = xsimd::simd_type::size;
std::size_t vec_size = size - size % simd_size;
for(std::size_t i = 0; i < vec_size; i += simd_size)
{
auto ba = xs::load_aligned(&a[i]);
auto bb = xs::load_aligned(&b[i]);
auto bres = (ba + bb) / 2.;
bres.store_aligned(&res[i]);
}
for(std::size_t i = vec_size; i < size; ++i)
{
res[i] = (a[i] + b[i]) / 2.;
}
}
```
## Building and Running the Tests
Building the tests requires [cmake](https://cmake.org).
`cmake` is available as a package for most linux distributions. Besides, they can also be installed with the `conda` package manager (even on windows):
```bash
conda install -c conda-forge cmake
```
Once `cmake` is installed, you can build and run the tests:
```bash
mkdir build
cd build
cmake ../ -DBUILD_TESTS=ON
make xtest
```
In the context of continuous integration with Travis CI, tests are run in a `conda` environment, which can be activated with
```bash
cd test
conda env create -f ./test-environment.yml
source activate test-xsimd
cd ..
cmake . -DBUILD_TESTS=ON
make xtest
```
## Building the HTML Documentation
xsimd's documentation is built with three tools
- [doxygen](http://www.doxygen.org)
- [sphinx](http://www.sphinx-doc.org)
- [breathe](https://breathe.readthedocs.io)
While doxygen must be installed separately, you can install breathe by typing
```bash
pip install breathe
```
Breathe can also be installed with `conda`
```bash
conda install -c conda-forge breathe
```
Finally, build the documentation with
```bash
make html
```
from the `docs` subdirectory.
## License
We use a shared copyright model that enables all contributors to maintain the
copyright on their contributions.
This software is licensed under the BSD-3-Clause license. See the [LICENSE](LICENSE) file for details.