Basic usage

Explicit use of an instruction set extension

Here is an example that computes the mean of two sets of 4 double floating point values, using the AVX extension:

#include <iostream>
#include "xsimd/xsimd.hpp"

namespace xs = xsimd;

int main(int argc, char* argv[])
{
    xs::batch<double, xs::avx> a = {1.5, 2.5, 3.5, 4.5};
    xs::batch<double, xs::avx> b = {2.5, 3.5, 4.5, 5.5};
    auto mean = (a + b) / 2;
    std::cout << mean << std::endl;
    return 0;
}

Note that in that case, the instruction set is explicilty specified in the batch type.

This example outputs:

(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, using a code that’s generic on the batch size:

#include <cstddef>
#include <vector>
#include "xsimd/xsimd.hpp"

namespace xs = xsimd;
using vector_type = std::vector<double, xsimd::aligned_allocator<double>>;

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<double>::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;
    }
}

In that case, the architecture is chosen based on the compilation flags, prioritizing the largest width and the most recent instruction set.