AMReX_GpuLaunchFunctsG.H

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#ifndef AMREX_GPU_LAUNCH_FUNCTS_G_H_
#define AMREX_GPU_LAUNCH_FUNCTS_G_H_
#include <AMReX_Config.H>
 
namespace amrex {
 
namespace detail {
 
    // call_f_scalar_handler
 
    template <typename F, typename N>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_scalar_handler (F const& f, N i, Gpu::Handler const&)
        noexcept -> decltype(f(0))
    {
        return f(i);
    }
 
    template <typename F, typename N>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_scalar_handler (F const& f, N i, Gpu::Handler const& handler)
        noexcept -> decltype(f(0,Gpu::Handler{}))
    {
        return f(i, handler);
    }
 
    // call_f_intvect_inner
 
    template <typename F, std::size_t...Ns, class...Args>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_inner (std::index_sequence<Ns...>, F const& f, IntVectND<1> iv, Args...args)
        noexcept -> decltype(f(0, 0, 0, args...))
    {
        return f(iv[0], 0, 0, args...);
    }
 
    template <typename F, std::size_t...Ns, class...Args>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_inner (std::index_sequence<Ns...>, F const& f, IntVectND<2> iv, Args...args)
        noexcept -> decltype(f(0, 0, 0, args...))
    {
        return f(iv[0], iv[1], 0, args...);
    }
 
    template <typename F, int dim, std::size_t...Ns, class...Args>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_inner (std::index_sequence<Ns...>, F const& f, IntVectND<dim> iv, Args...args)
        noexcept -> decltype(f(iv, args...))
    {
        return f(iv, args...);
    }
 
    template <typename F, int dim, std::size_t...Ns, class...Args>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_inner (std::index_sequence<Ns...>, F const& f, IntVectND<dim> iv, Args...args)
        noexcept -> decltype(f(iv[Ns]..., args...))
    {
        return f(iv[Ns]..., args...);
    }
 
    // call_f_intvect
 
    template <typename F, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect (F const& f, IntVectND<dim> iv)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv))
    {
        return call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv);
    }
 
    // call_f_intvect_engine
 
    template <typename F, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_engine (F const& f, IntVectND<dim> iv, RandomEngine engine)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, engine))
    {
        return call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, engine);
    }
 
    // call_f_intvect_handler
 
    template <typename F, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_handler (F const& f, IntVectND<dim> iv, Gpu::Handler const&)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv))
    {
        return call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv);
    }
 
    template <typename F, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_handler (F const& f, IntVectND<dim> iv, Gpu::Handler const& handler)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, Gpu::Handler{}))
    {
        return call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, handler);
    }
 
    // call_f_intvect_ncomp
 
    template <typename F, typename T, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_ncomp (F const& f, IntVectND<dim> iv, T ncomp)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, 0))
    {
        for (T n = 0; n < ncomp; ++n) {
            call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, n);
        }
    }
 
    // call_f_intvect_ncomp_engine
 
    template <typename F, typename T, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_ncomp_engine (F const& f, IntVectND<dim> iv, T ncomp, RandomEngine engine)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, 0, engine))
    {
        for (T n = 0; n < ncomp; ++n) {
            call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, n, engine);
        }
    }
 
    // call_f_intvect_ncomp_handler
 
    template <typename F, typename T, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_ncomp_handler (F const& f, IntVectND<dim> iv, T ncomp, Gpu::Handler const&)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, 0))
    {
        for (T n = 0; n < ncomp; ++n) {
            call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, n);
        }
    }
 
    template <typename F, typename T, int dim>
    AMREX_GPU_DEVICE AMREX_FORCE_INLINE
    auto call_f_intvect_ncomp_handler (F const& f, IntVectND<dim> iv, T ncomp, Gpu::Handler const& handler)
        noexcept -> decltype(call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, 0, Gpu::Handler{}))
    {
        for (T n = 0; n < ncomp; ++n) {
            call_f_intvect_inner(std::make_index_sequence<dim>(), f, iv, n, handler);
        }
    }
 
}
 
#ifdef AMREX_USE_SYCL
 
template <typename L>
void single_task (gpuStream_t stream, L const& f)
{
    detail::SyclKernelDevPtr<L> skdp(f, stream);
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    auto& q = *(stream.queue);
    try {
        q.submit([&] (sycl::handler& h) {
            if constexpr (detail::is_big_kernel<L>()) {
                h.single_task([=] () { (*pf)(); });
            } else {
                h.single_task(f);
            }
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("single_task: ")+ex.what()+"!!!!!");
    }
}
 
template<typename L>
void launch (int nblocks, int nthreads_per_block, std::size_t shared_mem_bytes,
             gpuStream_t stream, L const& f)
{
    detail::SyclKernelDevPtr<L> skdp(f, stream);
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const auto nthreads_total = std::size_t(nthreads_per_block) * nblocks;
    const std::size_t shared_mem_numull = (shared_mem_bytes+sizeof(unsigned long long)-1)
        / sizeof(unsigned long long);
    auto& q = *(stream.queue);
    try {
        q.submit([&] (sycl::handler& h) {
            sycl::local_accessor<unsigned long long>
                shared_data(sycl::range<1>(shared_mem_numull), h);
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                if constexpr (detail::is_big_kernel<L>()) {
                    (*pf)(Gpu::Handler{&item,shared_data.get_multi_ptr<sycl::access::decorated::yes>().get()});
                } else {
                    f(Gpu::Handler{&item,shared_data.get_multi_ptr<sycl::access::decorated::yes>().get()});
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("launch: ")+ex.what()+"!!!!!");
    }
}
 
template<typename L>
void launch (int nblocks, int nthreads_per_block, gpuStream_t stream, L const& f)
{
    detail::SyclKernelDevPtr<L> skdp(f, stream);
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const auto nthreads_total = std::size_t(nthreads_per_block) * nblocks;
    auto& q = *(stream.queue);
    try {
        q.submit([&] (sycl::handler& h) {
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                if constexpr (detail::is_big_kernel<L>()) {
                    (*pf)(item);
                } else {
                    f(item);
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("launch: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, typename L>
void launch (int nblocks, std::size_t shared_mem_bytes, gpuStream_t stream,
             L const& f)
{
    detail::SyclKernelDevPtr<L> skdp(f, stream);
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const auto nthreads_total = MT * std::size_t(nblocks);
    const std::size_t shared_mem_numull = (shared_mem_bytes+sizeof(unsigned long long)-1)
        / sizeof(unsigned long long);
    auto& q = *(stream.queue);
    try {
        q.submit([&] (sycl::handler& h) {
            sycl::local_accessor<unsigned long long>
                shared_data(sycl::range<1>(shared_mem_numull), h);
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(MT)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(MT)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                if constexpr (detail::is_big_kernel<L>()) {
                    (*pf)(Gpu::Handler{&item,shared_data.get_multi_ptr<sycl::access::decorated::yes>().get()});
                } else {
                    f(Gpu::Handler{&item,shared_data.get_multi_ptr<sycl::access::decorated::yes>().get()});
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("launch: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, typename L>
void launch (int nblocks, gpuStream_t stream, L const& f)
{
    detail::SyclKernelDevPtr<L> skdp(f, stream);
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const auto nthreads_total = MT * std::size_t(nblocks);
    auto& q = *(stream.queue);
    try {
        q.submit([&] (sycl::handler& h) {
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(MT)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(MT)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                if constexpr (detail::is_big_kernel<L>()) {
                    (*pf)(item);
                } else {
                    f(item);
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("launch: ")+ex.what()+"!!!!!");
    }
}
 
template<int MT, typename T, typename L>
void launch (T const& n, L const& f)
{
    if (amrex::isEmpty(n)) { return; }
 
    detail::SyclKernelDevPtr<L> skdp(f, Gpu::gpuStream());
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const auto ec = Gpu::makeExecutionConfig<MT>(n);
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        q.submit([&] (sycl::handler& h) {
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(MT)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                for (auto const i : Gpu::Range(n,item.get_global_id(0),item.get_global_range(0))) {
                    if constexpr (detail::is_big_kernel<L>()) {
                        (*pf)(i);
                    } else {
                        f(i);
                    }
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("launch: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, std::integral T, typename L>
void ParallelFor (Gpu::KernelInfo const& info, T n, L const& f)
{
    if (amrex::isEmpty(n)) { return; }
 
    detail::SyclKernelDevPtr<L> skdp(f, Gpu::gpuStream());
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const auto ec = Gpu::makeExecutionConfig<MT>(n);
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        if (info.hasReduction()) {
            q.submit([&] (sycl::handler& h) {
                sycl::local_accessor<unsigned long long>
                    shared_data(sycl::range<1>(std::max(Gpu::Device::warp_size,
                                                        MT/Gpu::Device::warp_size)), h);
                h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                                 sycl::range<1>(nthreads_per_block)),
                [=] (sycl::nd_item<1> item)
                [[sycl::reqd_work_group_size(MT)]]
                [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
                {
                    for (std::size_t i = item.get_global_id(0), stride = item.get_global_range(0);
                         i < std::size_t(n); i += stride) {
                        int n_active_threads = amrex::min(std::size_t(n)-i+item.get_local_id(0),
                                                          item.get_local_range(0));
                        if constexpr (detail::is_big_kernel<L>()) {
                            detail::call_f_scalar_handler(*pf, T(i),
                                                          Gpu::Handler{&item, shared_data.get_multi_ptr<sycl::access::decorated::yes>().get(),
                                                          n_active_threads});
                        } else {
                            detail::call_f_scalar_handler(f, T(i),
                                                          Gpu::Handler{&item, shared_data.get_multi_ptr<sycl::access::decorated::yes>().get(),
                                                          n_active_threads});
                        }
                    }
                });
            });
        } else {
            q.submit([&] (sycl::handler& h) {
                h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                                 sycl::range<1>(nthreads_per_block)),
                [=] (sycl::nd_item<1> item)
                [[sycl::reqd_work_group_size(MT)]]
                [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
                {
                    for (std::size_t i = item.get_global_id(0), stride = item.get_global_range(0);
                         i < std::size_t(n); i += stride) {
                        if constexpr (detail::is_big_kernel<L>()) {
                            detail::call_f_scalar_handler(*pf, T(i), Gpu::Handler{&item});
                        } else {
                            detail::call_f_scalar_handler(f, T(i), Gpu::Handler{&item});
                        }
                    }
                });
            });
        }
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, typename L, int dim>
void ParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
 
    detail::SyclKernelDevPtr<L> skdp(f, Gpu::gpuStream());
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const BoxIndexerND<dim> indexer(box);
    const auto ec = Gpu::makeExecutionConfig<MT>(box.numPts());
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        if (info.hasReduction()) {
            q.submit([&] (sycl::handler& h) {
                sycl::local_accessor<unsigned long long>
                    shared_data(sycl::range<1>(std::max(Gpu::Device::warp_size,
                                                        MT/Gpu::Device::warp_size)), h);
                h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                                 sycl::range<1>(nthreads_per_block)),
                [=] (sycl::nd_item<1> item)
                [[sycl::reqd_work_group_size(MT)]]
                [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
                {
                    for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                         icell < indexer.numPts(); icell += stride) {
                        auto iv = indexer.intVect(icell);
                        int n_active_threads = amrex::min(indexer.numPts()-icell+std::uint64_t(item.get_local_id(0)),
                                                          std::uint64_t(item.get_local_range(0)));
                        if constexpr (detail::is_big_kernel<L>()) {
                            detail::call_f_intvect_handler(*pf,
                                                           iv, Gpu::Handler{&item,
                                                               shared_data.get_multi_ptr<sycl::access::decorated::yes>().get(),
                                                               n_active_threads});
                        } else {
                            detail::call_f_intvect_handler(f,
                                                           iv, Gpu::Handler{&item,
                                                               shared_data.get_multi_ptr<sycl::access::decorated::yes>().get(),
                                                               n_active_threads});
                        }
                    }
                });
            });
        } else {
            q.submit([&] (sycl::handler& h) {
                h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                                 sycl::range<1>(nthreads_per_block)),
                [=] (sycl::nd_item<1> item)
                [[sycl::reqd_work_group_size(MT)]]
                [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
                {
                    for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                         icell < indexer.numPts(); icell += stride) {
                        auto iv = indexer.intVect(icell);
                        if constexpr (detail::is_big_kernel<L>()) {
                            detail::call_f_intvect_handler(*pf,iv,Gpu::Handler{&item});
                        } else {
                            detail::call_f_intvect_handler(f,iv,Gpu::Handler{&item});
                        }
                    }
                });
            });
        }
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, std::integral T, typename L, int dim>
void ParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
 
    detail::SyclKernelDevPtr<L> skdp(f, Gpu::gpuStream());
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const BoxIndexerND<dim> indexer(box);
    const auto ec = Gpu::makeExecutionConfig<MT>(box.numPts());
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        if (info.hasReduction()) {
            q.submit([&] (sycl::handler& h) {
                sycl::local_accessor<unsigned long long>
                    shared_data(sycl::range<1>(std::max(Gpu::Device::warp_size,
                                                        MT/Gpu::Device::warp_size)), h);
                h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                                 sycl::range<1>(nthreads_per_block)),
                [=] (sycl::nd_item<1> item)
                [[sycl::reqd_work_group_size(MT)]]
                [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
                {
                    for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                         icell < indexer.numPts(); icell += stride) {
                        auto iv = indexer.intVect(icell);
                        int n_active_threads = amrex::min(indexer.numPts()-icell+std::uint64_t(item.get_local_id(0)),
                                                          std::uint64_t(item.get_local_range(0)));
                        if constexpr (detail::is_big_kernel<L>()) {
                            detail::call_f_intvect_ncomp_handler(*pf, iv, ncomp,
                                                                 Gpu::Handler{&item, shared_data.get_multi_ptr<sycl::access::decorated::yes>().get(),
                                                                     n_active_threads});
                        } else {
                            detail::call_f_intvect_ncomp_handler(f, iv, ncomp,
                                                                 Gpu::Handler{&item, shared_data.get_multi_ptr<sycl::access::decorated::yes>().get(),
                                                                     n_active_threads});
                        }
                    }
                });
            });
        } else {
            q.submit([&] (sycl::handler& h) {
                h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                                 sycl::range<1>(nthreads_per_block)),
                [=] (sycl::nd_item<1> item)
                [[sycl::reqd_work_group_size(MT)]]
                [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
                {
                    for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                         icell < indexer.numPts(); icell += stride) {
                        auto iv = indexer.intVect(icell);
                        if constexpr (detail::is_big_kernel<L>()) {
                            detail::call_f_intvect_ncomp_handler(*pf,iv,ncomp,Gpu::Handler{&item});
                        } else {
                            detail::call_f_intvect_ncomp_handler(f,iv,ncomp,Gpu::Handler{&item});
                        }
                    }
                });
            });
        }
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <std::integral T, typename L>
void ParallelForRNG (T n, L const& f)
{
    if (amrex::isEmpty(n)) { return; }
 
    detail::SyclKernelDevPtr<L> skdp(f, Gpu::gpuStream());
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const auto ec = Gpu::ExecutionConfig(n);
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * amrex::min(ec.numBlocks.x,Gpu::Device::maxBlocksPerLaunch());
    auto& q = Gpu::Device::streamQueue();
    auto& engdescr = *(getRandEngineDescriptor());
    try {
        q.submit([&] (sycl::handler& h) {
            auto engine_acc = engdescr.get_access(h);
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(AMREX_GPU_MAX_THREADS)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                auto const tid = item.get_global_id(0);
                auto engine = engine_acc.load(tid);
                RandomEngine rand_eng{&engine};
                for (std::size_t i = tid, stride = item.get_global_range(0); i < std::size_t(n); i += stride) {
                    if constexpr (detail::is_big_kernel<L>()) {
                        (*pf)(T(i),rand_eng);
                    } else {
                        f(T(i),rand_eng);
                    }
                }
                engine_acc.store(engine, tid);
            });
        });
        q.wait_and_throw(); // because next launch might be on a different queue
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <typename L, int dim>
void ParallelForRNG (BoxND<dim> const& box, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
 
    detail::SyclKernelDevPtr<L> skdp(f, Gpu::gpuStream());
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const BoxIndexerND<dim> indexer(box);
    const auto ec = Gpu::ExecutionConfig(box.numPts());
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * amrex::min(ec.numBlocks.x,Gpu::Device::maxBlocksPerLaunch());
    auto& q = Gpu::Device::streamQueue();
    auto& engdescr = *(getRandEngineDescriptor());
    try {
        q.submit([&] (sycl::handler& h) {
            auto engine_acc = engdescr.get_access(h);
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(AMREX_GPU_MAX_THREADS)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                auto const tid = item.get_global_id(0);
                auto engine = engine_acc.load(tid);
                RandomEngine rand_eng{&engine};
                for (std::uint64_t icell = tid, stride = item.get_global_range(0);
                     icell < indexer.numPts(); icell += stride) {
                    auto iv = indexer.intVect(icell);
                    if constexpr (detail::is_big_kernel<L>()) {
                        detail::call_f_intvect_engine(*pf,iv,rand_eng);
                    } else {
                        detail::call_f_intvect_engine(f,iv,rand_eng);
                    }
                }
                engine_acc.store(engine, tid);
            });
        });
        q.wait_and_throw(); // because next launch might be on a different queue
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <std::integral T, typename L, int dim>
void ParallelForRNG (BoxND<dim> const& box, T ncomp, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
 
    detail::SyclKernelDevPtr<L> skdp(f, Gpu::gpuStream());
    L const* pf = skdp.template get<0>();
    amrex::ignore_unused(pf);
 
    const BoxIndexerND<dim> indexer(box);
    const auto ec = Gpu::ExecutionConfig(box.numPts());
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * amrex::min(ec.numBlocks.x,Gpu::Device::maxBlocksPerLaunch());
    auto& q = Gpu::Device::streamQueue();
    auto& engdescr = *(getRandEngineDescriptor());
    try {
        q.submit([&] (sycl::handler& h) {
            auto engine_acc = engdescr.get_access(h);
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(AMREX_GPU_MAX_THREADS)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                auto const tid = item.get_global_id(0);
                auto engine = engine_acc.load(tid);
                RandomEngine rand_eng{&engine};
                for (std::uint64_t icell = tid, stride = item.get_global_range(0);
                     icell < indexer.numPts(); icell += stride) {
                    auto iv = indexer.intVect(icell);
                    if constexpr (detail::is_big_kernel<L>()) {
                        detail::call_f_intvect_ncomp_engine(*pf,iv,ncomp,rand_eng);
                    } else {
                        detail::call_f_intvect_ncomp_engine(f,iv,ncomp,rand_eng);
                    }
                }
                engine_acc.store(engine, tid);
            });
        });
        q.wait_and_throw(); // because next launch might be on a different queue
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, typename L1, typename L2, int dim>
void ParallelFor (Gpu::KernelInfo const& /*info*/, BoxND<dim> const& box1, BoxND<dim> const& box2, L1 const& f1, L2 const& f2)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2)) { return; }
 
    detail::SyclKernelDevPtr<L1,L2> skdp(f1, f2, Gpu::gpuStream());
    L1 const* pf1 = skdp.template get<0>();
    L2 const* pf2 = skdp.template get<1>();
    amrex::ignore_unused(pf1,pf2);
 
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max(box1.numPts(), box2.numPts()));
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        q.submit([&] (sycl::handler& h) {
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(MT)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                auto const ncells = std::max(indexer1.numPts(), indexer2.numPts());
                for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                     icell < ncells; icell += stride) {
                    if (icell < indexer1.numPts()) {
                        auto iv = indexer1.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2>()) {
                            detail::call_f_intvect(*pf1,iv);
                        } else {
                            detail::call_f_intvect(f1,iv);
                        }
                    }
                    if (icell < indexer2.numPts()) {
                        auto iv = indexer2.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2>()) {
                            detail::call_f_intvect(*pf2,iv);
                        } else {
                            detail::call_f_intvect(f2,iv);
                        }
                    }
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
void ParallelFor (Gpu::KernelInfo const& /*info*/,
                  BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                  L1 const& f1, L2 const& f2, L3 const& f3)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2) && amrex::isEmpty(box3)) { return; }
 
    detail::SyclKernelDevPtr<L1,L2,L3> skdp(f1, f2, f3, Gpu::gpuStream());
    L1 const* pf1 = skdp.template get<0>();
    L2 const* pf2 = skdp.template get<1>();
    L3 const* pf3 = skdp.template get<2>();
    amrex::ignore_unused(pf1,pf2,pf3);
 
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const BoxIndexerND<dim> indexer3(box3);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max({box1.numPts(),box2.numPts(),box3.numPts()}));
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        q.submit([&] (sycl::handler& h) {
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(MT)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                auto const ncells = amrex::max(indexer1.numPts(), indexer2.numPts(), indexer3.numPts());
                for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                     icell < ncells; icell += stride) {
                    if (icell < indexer1.numPts()) {
                        auto iv = indexer1.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2,L3>()) {
                            detail::call_f_intvect(*pf1,iv);
                        } else {
                            detail::call_f_intvect(f1,iv);
                        }
                    }
                    if (icell < indexer2.numPts()) {
                        auto iv = indexer2.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2,L3>()) {
                            detail::call_f_intvect(*pf2,iv);
                        } else {
                            detail::call_f_intvect(f2,iv);
                        }
                    }
                    if (icell < indexer3.numPts()) {
                        auto iv = indexer3.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2,L3>()) {
                            detail::call_f_intvect(*pf3,iv);
                        } else {
                            detail::call_f_intvect(f3,iv);
                        }
                    }
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void ParallelFor (Gpu::KernelInfo const& /*info*/,
                  BoxND<dim> const& box1, T1 ncomp1, L1 const& f1,
                  BoxND<dim> const& box2, T2 ncomp2, L2 const& f2)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2)) { return; }
 
    detail::SyclKernelDevPtr<L1,L2> skdp(f1, f2, Gpu::gpuStream());
    L1 const* pf1 = skdp.template get<0>();
    L2 const* pf2 = skdp.template get<1>();
    amrex::ignore_unused(pf1,pf2);
 
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max(box1.numPts(),box2.numPts()));
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        q.submit([&] (sycl::handler& h) {
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(MT)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                auto const ncells = std::max(indexer1.numPts(), indexer2.numPts());
                for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                     icell < ncells; icell += stride) {
                    if (icell < indexer1.numPts()) {
                        auto iv = indexer1.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2>()) {
                            detail::call_f_intvect_ncomp(*pf1,iv,ncomp1);
                        } else {
                            detail::call_f_intvect_ncomp(f1,iv,ncomp1);
                        }
                    }
                    if (icell < indexer2.numPts()) {
                        auto iv = indexer2.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2>()) {
                            detail::call_f_intvect_ncomp(*pf2,iv,ncomp2);
                        } else {
                            detail::call_f_intvect_ncomp(f2,iv,ncomp2);
                        }
                    }
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void ParallelFor (Gpu::KernelInfo const& /*info*/,
                  BoxND<dim> const& box1, T1 ncomp1, L1 const& f1,
                  BoxND<dim> const& box2, T2 ncomp2, L2 const& f2,
                  BoxND<dim> const& box3, T3 ncomp3, L3 const& f3)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2) && amrex::isEmpty(box3)) { return; }
 
    detail::SyclKernelDevPtr<L1,L2,L3> skdp(f1, f2, f3, Gpu::gpuStream());
    L1 const* pf1 = skdp.template get<0>();
    L2 const* pf2 = skdp.template get<1>();
    L3 const* pf3 = skdp.template get<2>();
    amrex::ignore_unused(pf1,pf2,pf3);
 
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const BoxIndexerND<dim> indexer3(box3);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max({box1.numPts(),box2.numPts(),box3.numPts()}));
    const auto nthreads_per_block = ec.numThreads.x;
    const auto nthreads_total = std::size_t(nthreads_per_block) * ec.numBlocks.x;
    auto& q = Gpu::Device::streamQueue();
    try {
        q.submit([&] (sycl::handler& h) {
            h.parallel_for(sycl::nd_range<1>(sycl::range<1>(nthreads_total),
                                             sycl::range<1>(nthreads_per_block)),
            [=] (sycl::nd_item<1> item)
            [[sycl::reqd_work_group_size(MT)]]
            [[sycl::reqd_sub_group_size(Gpu::Device::warp_size)]]
            {
                auto const ncells = amrex::max(indexer1.numPts(), indexer2.numPts(), indexer3.numPts());
                for (std::uint64_t icell = item.get_global_id(0), stride = item.get_global_range(0);
                     icell < ncells; icell += stride) {
                    if (icell < indexer1.numPts()) {
                        auto iv = indexer1.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2,L3>()) {
                            detail::call_f_intvect_ncomp(*pf1,iv,ncomp1);
                        } else {
                            detail::call_f_intvect_ncomp(f1,iv,ncomp1);
                        }
                    }
                    if (icell < indexer2.numPts()) {
                        auto iv = indexer2.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2,L3>()) {
                            detail::call_f_intvect_ncomp(*pf2,iv,ncomp2);
                        } else {
                            detail::call_f_intvect_ncomp(f2,iv,ncomp2);
                        }
                    }
                    if (icell < indexer3.numPts()) {
                        auto iv = indexer3.intVect(icell);
                        if constexpr (detail::is_big_kernel<L1,L2,L3>()) {
                            detail::call_f_intvect_ncomp(*pf3,iv,ncomp3);
                        } else {
                            detail::call_f_intvect_ncomp(f3,iv,ncomp3);
                        }
                    }
                }
            });
        });
    } catch (sycl::exception const& ex) {
        amrex::Abort(std::string("ParallelFor: ")+ex.what()+"!!!!!");
    }
}
 
#else
// CUDA or HIP
 
template <typename L>
void single_task (gpuStream_t stream, L const& f)
{
    AMREX_LAUNCH_KERNEL(Gpu::Device::warp_size, 1, 1, 0, stream, f);
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, typename L>
void launch (int nblocks, std::size_t shared_mem_bytes, gpuStream_t stream,
             L const& f)
{
    AMREX_LAUNCH_KERNEL(MT, nblocks, MT, shared_mem_bytes, stream, f);
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, typename L>
void launch (int nblocks, gpuStream_t stream, L const& f)
{
    AMREX_LAUNCH_KERNEL(MT, nblocks, MT, 0, stream, f);
    AMREX_GPU_ERROR_CHECK();
}
 
template<typename L>
void launch (int nblocks, int nthreads_per_block, std::size_t shared_mem_bytes,
             gpuStream_t stream, L const& f)
{
    AMREX_LAUNCH_KERNEL_NOBOUND(nblocks, nthreads_per_block, shared_mem_bytes, stream, f);
    AMREX_GPU_ERROR_CHECK();
}
 
template<typename L>
void launch (int nblocks, int nthreads_per_block, gpuStream_t stream, L&& f) noexcept
{
    launch(nblocks, nthreads_per_block, 0, stream, std::forward<L>(f));
}
 
template<int MT, std::integral T, typename L>
void launch (T const& n, L const& f)
{
    static_assert(sizeof(T) >= 2);
    if (amrex::isEmpty(n)) { return; }
    const auto& nec = Gpu::makeNExecutionConfigs<MT>(n);
    for (auto const& ec : nec) {
        const T start_idx = T(ec.start_idx);
        const T nleft = n - start_idx;
        AMREX_LAUNCH_KERNEL(MT, ec.nblocks, MT, 0, Gpu::gpuStream(),
        [=] AMREX_GPU_DEVICE () noexcept {
            // This will not overflow, even though nblocks*MT might.
            auto tid = T(MT)*T(blockIdx.x)+T(threadIdx.x);
            if (tid < nleft) {
                f(tid+start_idx);
            }
        });
    }
    AMREX_GPU_ERROR_CHECK();
}
 
template<int MT, int dim, typename L>
void launch (BoxND<dim> const& box, L const& f)
{
    if (box.isEmpty()) { return; }
    const auto& nec = Gpu::makeNExecutionConfigs<MT>(box);
    const BoxIndexerND<dim> indexer(box);
    const auto type = box.ixType();
    for (auto const& ec : nec) {
        const auto start_idx = std::uint64_t(ec.start_idx);
        AMREX_LAUNCH_KERNEL(MT, ec.nblocks, MT, 0, Gpu::gpuStream(),
        [=] AMREX_GPU_DEVICE () noexcept {
            auto icell = std::uint64_t(MT)*blockIdx.x+threadIdx.x + start_idx;
            if (icell < indexer.numPts()) {
                auto iv = indexer.intVect(icell);
                f(BoxND<dim>(iv,iv,type));
            }
        });
    }
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, std::integral T, typename L>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelFor (Gpu::KernelInfo const&, T n, L const& f)
{
    static_assert(sizeof(T) >= 2);
    if (amrex::isEmpty(n)) { return; }
    const auto& nec = Gpu::makeNExecutionConfigs<MT>(n);
    for (auto const& ec : nec) {
        const T start_idx = T(ec.start_idx);
        const T nleft = n - start_idx;
        AMREX_LAUNCH_KERNEL(MT, ec.nblocks, MT, 0, Gpu::gpuStream(),
        [=] AMREX_GPU_DEVICE () noexcept {
            // This will not overflow, even though nblocks*MT might.
            auto tid = T(MT)*T(blockIdx.x)+T(threadIdx.x);
            if (tid < nleft) {
                detail::call_f_scalar_handler(f, tid+start_idx,
                    Gpu::Handler(amrex::min((std::uint64_t(nleft-tid)+(std::uint64_t)threadIdx.x),
                    (std::uint64_t)MT)));
            }
        });
    }
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, typename L, int dim>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelFor (Gpu::KernelInfo const&, BoxND<dim> const& box, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
    const BoxIndexerND<dim> indexer(box);
    const auto& nec = Gpu::makeNExecutionConfigs<MT>(box);
    for (auto const& ec : nec) {
        const auto start_idx = std::uint64_t(ec.start_idx);
        AMREX_LAUNCH_KERNEL(MT, ec.nblocks, MT, 0, Gpu::gpuStream(),
        [=] AMREX_GPU_DEVICE () noexcept {
            auto icell = std::uint64_t(MT)*blockIdx.x+threadIdx.x + start_idx;
            if (icell < indexer.numPts()) {
                auto iv = indexer.intVect(icell);
                detail::call_f_intvect_handler(f, iv,
                    Gpu::Handler(amrex::min((indexer.numPts()-icell+(std::uint64_t)threadIdx.x),
                    (std::uint64_t)MT)));
            }
        });
    }
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, std::integral T, typename L, int dim>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelFor (Gpu::KernelInfo const&, BoxND<dim> const& box, T ncomp, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
    const BoxIndexerND<dim> indexer(box);
    const auto& nec = Gpu::makeNExecutionConfigs<MT>(box);
    for (auto const& ec : nec) {
        const auto start_idx = std::uint64_t(ec.start_idx);
        AMREX_LAUNCH_KERNEL(MT, ec.nblocks, MT, 0, Gpu::gpuStream(),
        [=] AMREX_GPU_DEVICE () noexcept {
            auto icell = std::uint64_t(MT)*blockIdx.x+threadIdx.x + start_idx;
            if (icell < indexer.numPts()) {
                auto iv = indexer.intVect(icell);
                detail::call_f_intvect_ncomp_handler(f, iv, ncomp,
                    Gpu::Handler(amrex::min((indexer.numPts()-icell+(std::uint64_t)threadIdx.x),
                    (std::uint64_t)MT)));
            }
        });
    }
    AMREX_GPU_ERROR_CHECK();
}
 
template <std::integral T, typename L>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelForRNG (T n, L const& f)
{
    if (amrex::isEmpty(n)) { return; }
    randState_t* rand_state = getRandState();
    const auto ec = Gpu::ExecutionConfig(n);
    AMREX_LAUNCH_KERNEL(AMREX_GPU_MAX_THREADS,
                        amrex::min(ec.numBlocks.x, Gpu::Device::maxBlocksPerLaunch()),
                        ec.numThreads, 0, Gpu::gpuStream(),
    [=] AMREX_GPU_DEVICE () noexcept {
        Long tid = Long(AMREX_GPU_MAX_THREADS)*blockIdx.x+threadIdx.x;
        RandomEngine engine{&(rand_state[tid])};
        for (Long i = tid, stride = Long(AMREX_GPU_MAX_THREADS)*gridDim.x; i < Long(n); i += stride) {
            f(T(i),engine);
        }
    });
    Gpu::streamSynchronize(); // To avoid multiple streams using RNG
    AMREX_GPU_ERROR_CHECK();
}
 
template <typename L, int dim>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelForRNG (BoxND<dim> const& box, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
    randState_t* rand_state = getRandState();
    const BoxIndexerND<dim> indexer(box);
    const auto ec = Gpu::ExecutionConfig(box.numPts());
    AMREX_LAUNCH_KERNEL(AMREX_GPU_MAX_THREADS,
                        amrex::min(ec.numBlocks.x, Gpu::Device::maxBlocksPerLaunch()),
                        ec.numThreads, 0, Gpu::gpuStream(),
    [=] AMREX_GPU_DEVICE () noexcept {
        auto const tid = std::uint64_t(AMREX_GPU_MAX_THREADS)*blockIdx.x+threadIdx.x;
        RandomEngine engine{&(rand_state[tid])};
        for (std::uint64_t icell = tid, stride = std::uint64_t(AMREX_GPU_MAX_THREADS)*gridDim.x; icell < indexer.numPts(); icell += stride) {
            auto iv = indexer.intVect(icell);
            detail::call_f_intvect_engine(f, iv, engine);
        }
    });
    Gpu::streamSynchronize(); // To avoid multiple streams using RNG
    AMREX_GPU_ERROR_CHECK();
}
 
template <std::integral T, typename L, int dim>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelForRNG (BoxND<dim> const& box, T ncomp, L const& f)
{
    if (amrex::isEmpty(box)) { return; }
    randState_t* rand_state = getRandState();
    const BoxIndexerND<dim> indexer(box);
    const auto ec = Gpu::ExecutionConfig(box.numPts());
    AMREX_LAUNCH_KERNEL(AMREX_GPU_MAX_THREADS,
                        amrex::min(ec.numBlocks.x, Gpu::Device::maxBlocksPerLaunch()),
                        ec.numThreads, 0, Gpu::gpuStream(),
    [=] AMREX_GPU_DEVICE () noexcept {
        auto const tid = std::uint64_t(AMREX_GPU_MAX_THREADS)*blockIdx.x+threadIdx.x;
        RandomEngine engine{&(rand_state[tid])};
        for (std::uint64_t icell = tid, stride = std::uint64_t(AMREX_GPU_MAX_THREADS)*gridDim.x; icell < indexer.numPts(); icell += stride) {
            auto iv = indexer.intVect(icell);
            detail::call_f_intvect_ncomp_engine(f, iv, ncomp, engine);
        }
    });
    Gpu::streamSynchronize(); // To avoid multiple streams using RNG
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, typename L1, typename L2, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value)
void
ParallelFor (Gpu::KernelInfo const&,
             BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2)) { return; }
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max(box1.numPts(),box2.numPts()));
    AMREX_LAUNCH_KERNEL(MT, ec.numBlocks, ec.numThreads, 0, Gpu::gpuStream(),
    [=] AMREX_GPU_DEVICE () noexcept {
        auto const ncells = std::max(indexer1.numPts(), indexer2.numPts());
        for (std::uint64_t icell = std::uint64_t(MT)*blockIdx.x+threadIdx.x, stride = std::uint64_t(MT)*gridDim.x;
             icell < ncells; icell += stride) {
            if (icell < indexer1.numPts()) {
                auto iv = indexer1.intVect(icell);
                detail::call_f_intvect(f1, iv);
            }
            if (icell < indexer2.numPts()) {
                auto iv = indexer2.intVect(icell);
                detail::call_f_intvect(f2, iv);
            }
        }
    });
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value &&
          MaybeDeviceRunnable<L3>::value)
void
ParallelFor (Gpu::KernelInfo const&,
             BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
             L1&& f1, L2&& f2, L3&& f3)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2) && amrex::isEmpty(box3)) { return; }
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const BoxIndexerND<dim> indexer3(box3);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max({box1.numPts(),box2.numPts(),box3.numPts()}));
    AMREX_LAUNCH_KERNEL(MT, ec.numBlocks, ec.numThreads, 0, Gpu::gpuStream(),
    [=] AMREX_GPU_DEVICE () noexcept {
        auto const ncells = std::max({indexer1.numPts(), indexer2.numPts(), indexer3.numPts()});
        for (std::uint64_t icell = std::uint64_t(MT)*blockIdx.x+threadIdx.x, stride = std::uint64_t(MT)*gridDim.x;
             icell < ncells; icell += stride) {
            if (icell < indexer1.numPts()) {
                auto iv = indexer1.intVect(icell);
                detail::call_f_intvect(f1, iv);
            }
            if (icell < indexer2.numPts()) {
                auto iv = indexer2.intVect(icell);
                detail::call_f_intvect(f2, iv);
            }
            if (icell < indexer3.numPts()) {
                auto iv = indexer3.intVect(icell);
                detail::call_f_intvect(f3, iv);
            }
        }
    });
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value)
void
ParallelFor (Gpu::KernelInfo const&,
             BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
             BoxND<dim> const& box2, T2 ncomp2, L2&& f2)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2)) { return; }
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max(box1.numPts(),box2.numPts()));
    AMREX_LAUNCH_KERNEL(MT, ec.numBlocks, ec.numThreads, 0, Gpu::gpuStream(),
    [=] AMREX_GPU_DEVICE () noexcept {
        auto const ncells = std::max(indexer1.numPts(), indexer2.numPts());
        for (std::uint64_t icell = std::uint64_t(MT)*blockIdx.x+threadIdx.x, stride = std::uint64_t(MT)*gridDim.x;
             icell < ncells; icell += stride) {
            if (icell < indexer1.numPts()) {
                auto iv = indexer1.intVect(icell);
                detail::call_f_intvect_ncomp(f1, iv, ncomp1);
            }
            if (icell < indexer2.numPts()) {
                auto iv = indexer2.intVect(icell);
                detail::call_f_intvect_ncomp(f2, iv, ncomp2);
            }
        }
    });
    AMREX_GPU_ERROR_CHECK();
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value &&
          MaybeDeviceRunnable<L3>::value)
void
ParallelFor (Gpu::KernelInfo const&,
             BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
             BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
             BoxND<dim> const& box3, T3 ncomp3, L3&& f3)
{
    if (amrex::isEmpty(box1) && amrex::isEmpty(box2) && amrex::isEmpty(box3)) { return; }
    const BoxIndexerND<dim> indexer1(box1);
    const BoxIndexerND<dim> indexer2(box2);
    const BoxIndexerND<dim> indexer3(box3);
    const auto ec = Gpu::makeExecutionConfig<MT>(std::max({box1.numPts(),box2.numPts(),box3.numPts()}));
    AMREX_LAUNCH_KERNEL(MT, ec.numBlocks, ec.numThreads, 0, Gpu::gpuStream(),
    [=] AMREX_GPU_DEVICE () noexcept {
        auto const ncells = std::max({indexer1.numPts(), indexer2.numPts(), indexer3.numPts()});
        for (std::uint64_t icell = std::uint64_t(MT)*blockIdx.x+threadIdx.x, stride = std::uint64_t(MT)*gridDim.x;
             icell < ncells; icell += stride) {
            if (icell < indexer1.numPts()) {
                auto iv = indexer1.intVect(icell);
                detail::call_f_intvect_ncomp(f1, iv, ncomp1);
            }
            if (icell < indexer2.numPts()) {
                auto iv = indexer2.intVect(icell);
                detail::call_f_intvect_ncomp(f2, iv, ncomp2);
            }
            if (icell < indexer3.numPts()) {
                auto iv = indexer3.intVect(icell);
                detail::call_f_intvect_ncomp(f3, iv, ncomp3);
            }
        }
    });
    AMREX_GPU_ERROR_CHECK();
}
 
#endif
 
template <typename L>
void single_task (L&& f) noexcept
{
    single_task(Gpu::gpuStream(), std::forward<L>(f));
}
 
template<typename T, typename L>
void launch (T const& n, L&& f) noexcept
{
    launch<AMREX_GPU_MAX_THREADS>(n, std::forward<L>(f));
}
 
template <std::integral T, typename L>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelFor (Gpu::KernelInfo const& info, T n, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, n, std::forward<L>(f));
}
 
template <typename L, int dim>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, box, std::forward<L>(f));
}
 
template <std::integral T, typename L, int dim>
requires (MaybeDeviceRunnable<L>::value)
void
ParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, box, ncomp, std::forward<L>(f));
}
 
template <typename L1, typename L2, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value)
void
ParallelFor (Gpu::KernelInfo const& info,
             BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, box1, box2, std::forward<L1>(f1),
                                       std::forward<L2>(f2));
}
 
template <typename L1, typename L2, typename L3, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value &&
          MaybeDeviceRunnable<L3>::value)
void
ParallelFor (Gpu::KernelInfo const& info,
             BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
             L1&& f1, L2&& f2, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, box1, box2, box3, std::forward<L1>(f1),
                                       std::forward<L2>(f2), std::forward<L3>(f3));
}
 
template <std::integral T1, std::integral T2, typename L1, typename L2, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value)
void
ParallelFor (Gpu::KernelInfo const& info,
             BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
             BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, box1, ncomp1, std::forward<L1>(f1),
                                             box2, ncomp2, std::forward<L2>(f2));
}
 
template <std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
requires (MaybeDeviceRunnable<L1>::value && MaybeDeviceRunnable<L2>::value &&
          MaybeDeviceRunnable<L3>::value)
void
ParallelFor (Gpu::KernelInfo const& info,
             BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
             BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
             BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, box1, ncomp1, std::forward<L1>(f1),
                                             box2, ncomp2, std::forward<L2>(f2),
                                             box3, ncomp3, std::forward<L3>(f3));
}
 
template <std::integral T, typename L>
void For (Gpu::KernelInfo const& info, T n, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, n,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L>
void For (Gpu::KernelInfo const& info, T n, L&& f) noexcept
{
    ParallelFor<MT>(info, n,std::forward<L>(f));
}
 
template <typename L, int dim>
void For (Gpu::KernelInfo const& info, BoxND<dim> const& box, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info, box,std::forward<L>(f));
}
 
template <int MT, typename L, int dim>
void For (Gpu::KernelInfo const& info, BoxND<dim> const& box, L&& f) noexcept
{
    ParallelFor<MT>(info, box,std::forward<L>(f));
}
 
template <std::integral T, typename L, int dim>
void For (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info,box,ncomp,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L, int dim>
void For (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    ParallelFor<MT>(info,box,ncomp,std::forward<L>(f));
}
 
template <typename L1, typename L2, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <int MT, typename L1, typename L2, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    ParallelFor<MT>(info,box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <typename L1, typename L2, typename L3, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
          L1&& f1, L2&& f2, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,box2,box3,std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
          L1&& f1, L2&& f2, L3&& f3) noexcept
{
    ParallelFor<MT>(info,box1,box2,box3,std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    ParallelFor<MT>(info,box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
          BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(info,
                box1,ncomp1,std::forward<L1>(f1),
                box2,ncomp2,std::forward<L2>(f2),
                box3,ncomp3,std::forward<L3>(f3));
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void For (Gpu::KernelInfo const& info,
          BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
          BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    ParallelFor<MT>(info,
                box1,ncomp1,std::forward<L1>(f1),
                box2,ncomp2,std::forward<L2>(f2),
                box3,ncomp3,std::forward<L3>(f3));
}
 
template <std::integral T, typename L>
void ParallelFor (T n, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{}, n, std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L>
void ParallelFor (T n, L&& f) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{}, n, std::forward<L>(f));
}
 
template <typename L, int dim>
void ParallelFor (BoxND<dim> const& box, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{}, box, std::forward<L>(f));
}
 
template <int MT, typename L, int dim>
void ParallelFor (BoxND<dim> const& box, L&& f) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{}, box, std::forward<L>(f));
}
 
template <std::integral T, typename L, int dim>
void ParallelFor (BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box,ncomp,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L, int dim>
void ParallelFor (BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box,ncomp,std::forward<L>(f));
}
 
template <typename L1, typename L2, int dim>
void ParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <int MT, typename L1, typename L2, int dim>
void ParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <typename L1, typename L2, typename L3, int dim>
void ParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                  L1&& f1, L2&& f2, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,box2,box3,std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
void ParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                  L1&& f1, L2&& f2, L3&& f3) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box1,box2,box3,std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void ParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                  BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void ParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                  BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void ParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                  BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                  BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},
                box1,ncomp1,std::forward<L1>(f1),
                box2,ncomp2,std::forward<L2>(f2),
                box3,ncomp3,std::forward<L3>(f3));
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void ParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                  BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                  BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},
                box1,ncomp1,std::forward<L1>(f1),
                box2,ncomp2,std::forward<L2>(f2),
                box3,ncomp3,std::forward<L3>(f3));
}
 
template <std::integral T, typename L>
void For (T n, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{}, n,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L>
void For (T n, L&& f) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{}, n,std::forward<L>(f));
}
 
template <typename L, int dim>
void For (BoxND<dim> const& box, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{}, box,std::forward<L>(f));
}
 
template <int MT, typename L, int dim>
void For (BoxND<dim> const& box, L&& f) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{}, box,std::forward<L>(f));
}
 
template <std::integral T, typename L, int dim>
void For (BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box,ncomp,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L, int dim>
void For (BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box,ncomp,std::forward<L>(f));
}
 
template <typename L1, typename L2, int dim>
void For (BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <int MT, typename L1, typename L2, int dim>
void For (BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <typename L1, typename L2, typename L3, int dim>
void For (BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
          L1&& f1, L2&& f2, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,box2,box3,std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
void For (BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
          L1&& f1, L2&& f2, L3&& f3) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box1,box2,box3,std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void For (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void For (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void For (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
          BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    ParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},
                box1,ncomp1,std::forward<L1>(f1),
                box2,ncomp2,std::forward<L2>(f2),
                box3,ncomp3,std::forward<L3>(f3));
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void For (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
          BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
          BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    ParallelFor<MT>(Gpu::KernelInfo{},
                box1,ncomp1,std::forward<L1>(f1),
                box2,ncomp2,std::forward<L2>(f2),
                box3,ncomp3,std::forward<L3>(f3));
}
 
template <std::integral T, typename L>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info, T n, L&& f)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<AMREX_GPU_MAX_THREADS>(info,n,std::forward<L>(f));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        AMREX_PRAGMA_SIMD
        for (T i = 0; i < n; ++i) { f(i); }
#endif
    }
}
 
template <int MT, std::integral T, typename L>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info, T n, L&& f)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<MT>(info,n,std::forward<L>(f));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        AMREX_PRAGMA_SIMD
        for (T i = 0; i < n; ++i) { f(i); }
#endif
    }
}
 
template <std::integral T, typename L>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (T n, L&& f) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{}, n, std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (T n, L&& f) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{}, n, std::forward<L>(f));
}
 
template <typename L, int dim>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, L&& f)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<AMREX_GPU_MAX_THREADS>(info, box,std::forward<L>(f));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box,std::forward<L>(f));
#endif
    }
}
 
template <int MT, typename L, int dim>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, L&& f)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<MT>(info, box,std::forward<L>(f));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box,std::forward<L>(f));
#endif
    }
}
 
template <std::integral T, typename L, int dim>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L&& f)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<AMREX_GPU_MAX_THREADS>(info, box,ncomp,std::forward<L>(f));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box,ncomp,std::forward<L>(f));
#endif
    }
}
 
template <int MT, std::integral T, typename L, int dim>
requires (MaybeHostDeviceRunnable<L>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L&& f)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<MT>(info, box,ncomp,std::forward<L>(f));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box,ncomp,std::forward<L>(f));
#endif
    }
}
 
template <typename L1, typename L2, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,std::forward<L2>(f2));
#endif
    }
}
 
template <int MT, typename L1, typename L2, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<MT>(info,box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,std::forward<L2>(f2));
#endif
    }
}
 
template <typename L1, typename L2, typename L3, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value &&
          MaybeHostDeviceRunnable<L3>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                       L1&& f1, L2&& f2, L3&& f3)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,box2,box3,
                    std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,std::forward<L2>(f2));
        LoopConcurrentOnCpu(box3,std::forward<L3>(f3));
#endif
    }
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value &&
          MaybeHostDeviceRunnable<L3>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                       L1&& f1, L2&& f2, L3&& f3)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<MT>(info,box1,box2,box3,
                    std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,std::forward<L2>(f2));
        LoopConcurrentOnCpu(box3,std::forward<L3>(f3));
#endif
    }
}
 
template <std::integral T1, std::integral T2, typename L1, typename L2, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                       BoxND<dim> const& box2, T2 ncomp2, L2&& f2)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,ncomp1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,ncomp2,std::forward<L2>(f2));
#endif
    }
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                       BoxND<dim> const& box2, T2 ncomp2, L2&& f2)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<MT>(info,box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,ncomp1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,ncomp2,std::forward<L2>(f2));
#endif
    }
}
 
template <std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value &&
          MaybeHostDeviceRunnable<L3>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                       BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                       BoxND<dim> const& box3, T3 ncomp3, L3&& f3)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<AMREX_GPU_MAX_THREADS>(info,
                    box1,ncomp1,std::forward<L1>(f1),
                    box2,ncomp2,std::forward<L2>(f2),
                    box3,ncomp3,std::forward<L3>(f3));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,ncomp1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,ncomp2,std::forward<L2>(f2));
        LoopConcurrentOnCpu(box3,ncomp3,std::forward<L3>(f3));
#endif
    }
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
requires (MaybeHostDeviceRunnable<L1>::value && MaybeHostDeviceRunnable<L2>::value &&
          MaybeHostDeviceRunnable<L3>::value)
void
HostDeviceParallelFor (Gpu::KernelInfo const& info,
                       BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                       BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                       BoxND<dim> const& box3, T3 ncomp3, L3&& f3)
{
    if (Gpu::inLaunchRegion()) {
        ParallelFor<MT>(info,
                    box1,ncomp1,std::forward<L1>(f1),
                    box2,ncomp2,std::forward<L2>(f2),
                    box3,ncomp3,std::forward<L3>(f3));
    } else {
#ifdef AMREX_USE_SYCL
        amrex::Abort("amrex:: HOST_DEVICE disabled for Intel.  It takes too long to compile");
#else
        LoopConcurrentOnCpu(box1,ncomp1,std::forward<L1>(f1));
        LoopConcurrentOnCpu(box2,ncomp2,std::forward<L2>(f2));
        LoopConcurrentOnCpu(box3,ncomp3,std::forward<L3>(f3));
#endif
    }
}
 
template <std::integral T, typename L>
void HostDeviceFor (Gpu::KernelInfo const& info, T n, L&& f) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(info,n,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L>
void HostDeviceFor (Gpu::KernelInfo const& info, T n, L&& f) noexcept
{
    HostDeviceParallelFor<MT>(info,n,std::forward<L>(f));
}
 
template <typename L, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, L&& f) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(info,box,std::forward<L>(f));
}
 
template <int MT, typename L, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, L&& f) noexcept
{
    HostDeviceParallelFor<MT>(info,box,std::forward<L>(f));
}
 
template <std::integral T, typename L, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(info,box,ncomp,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info, BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    HostDeviceParallelFor<MT>(info,box,ncomp,std::forward<L>(f));
}
 
template <typename L1, typename L2, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <int MT, typename L1, typename L2, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    HostDeviceParallelFor<MT>(info,box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <typename L1, typename L2, typename L3, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                    L1&& f1, L2&& f2, L3&& f3) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(info, box1,box2,box3,
                          std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                    L1&& f1, L2&& f2, L3&& f3) noexcept
{
    HostDeviceParallelFor<MT>(info, box1,box2,box3,
                          std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                    BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(info,box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                    BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    HostDeviceParallelFor<MT>(info,box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                    BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                    BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(info,
                          box1,ncomp1,std::forward<L1>(f1),
                          box2,ncomp2,std::forward<L2>(f2),
                          box3,ncomp3,std::forward<L3>(f3));
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void HostDeviceFor (Gpu::KernelInfo const& info,
                    BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                    BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                    BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    HostDeviceParallelFor<MT>(info,
                          box1,ncomp1,std::forward<L1>(f1),
                          box2,ncomp2,std::forward<L2>(f2),
                          box3,ncomp3,std::forward<L3>(f3));
}
 
template <std::integral T, typename L>
void HostDeviceParallelFor (T n, L&& f) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},n,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L>
void HostDeviceParallelFor (T n, L&& f) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{},n,std::forward<L>(f));
}
 
template <typename L, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box, L&& f) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box,std::forward<L>(f));
}
 
template <int MT, typename L, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box, L&& f) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{},box,std::forward<L>(f));
}
 
template <std::integral T, typename L, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box,ncomp,std::forward<L>(f));
}
 
template <int MT, std::integral T, typename L, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box, T ncomp, L&& f) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{},box,ncomp,std::forward<L>(f));
}
 
template <typename L1, typename L2, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <int MT, typename L1, typename L2, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, L1&& f1, L2&& f2) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{},box1,box2,std::forward<L1>(f1),std::forward<L2>(f2));
}
 
template <typename L1, typename L2, typename L3, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                            L1&& f1, L2&& f2, L3&& f3) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{}, box1,box2,box3,
                          std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <int MT, typename L1, typename L2, typename L3, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, BoxND<dim> const& box2, BoxND<dim> const& box3,
                            L1&& f1, L2&& f2, L3&& f3) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{}, box1,box2,box3,
                          std::forward<L1>(f1),std::forward<L2>(f2),std::forward<L3>(f3));
}
 
template <std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                            BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <int MT, std::integral T1, std::integral T2, typename L1, typename L2, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                            BoxND<dim> const& box2, T2 ncomp2, L2&& f2) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{},box1,ncomp1,std::forward<L1>(f1),box2,ncomp2,std::forward<L2>(f2));
}
 
template <std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                            BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                            BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    HostDeviceParallelFor<AMREX_GPU_MAX_THREADS>(Gpu::KernelInfo{},
                          box1,ncomp1,std::forward<L1>(f1),
                          box2,ncomp2,std::forward<L2>(f2),
                          box3,ncomp3,std::forward<L3>(f3));
}
 
template <int MT, std::integral T1, std::integral T2, std::integral T3, typename L1, typename L2, typename L3, int dim>
void HostDeviceParallelFor (BoxND<dim> const& box1, T1 ncomp1, L1&& f1,
                            BoxND<dim> const& box2, T2 ncomp2, L2&& f2,
                            BoxND<dim> const& box3, T3 ncomp3, L3&& f3) noexcept
{
    HostDeviceParallelFor<MT>(Gpu::KernelInfo{},
                          box1,ncomp1,std::forward<L1>(f1),
                          box2,ncomp2,std::forward<L2>(f2),
                          box3,ncomp3,std::forward<L3>(f3));
}
 
}
 
#endif