#include <boost/math/quadrature/exp_sinh.hpp>

    namespace boost{ namespace math{ namespace quadrature {

    template <class F, class Real, class Policy = policies::policy<> >
    __device__ auto exp_sinh_integrate(const F& f, Real a, Real b, Real tolerance, Real* error, Real* L1, boost::math::size_t* levels)

    template <class F, class Real, class Policy = policies::policy<> >
    __device__ auto exp_sinh_integrate(const F& f, Real tolerance, Real* error, Real* L1, boost::math::size_t* levels)

}}}

Quadrature is additionally able to run on CUDA (NVCC and NVRTC) platforms. The major difference is outlined in the above function signatures. When used on device these are free standing functions instead of using OOP like on the host. The tables of abscissas and weights are stored in shared read only memory on the device instead of being initialized when the class is constructed. An example use case would be in the finite elements method computing a stiffness matrix since it would consist of many different functions.