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Implement parallel cuda::std::iota
This implements the `iota` algorithm for the cuda backend. * `std::iota` see https://en.cppreference.com/w/cpp/algorithm/iota.html It provides tests and benchmarks similar to Thrust and some boilerplate for libcu++ The functionality is publicly available yet and implemented in a private internal header Fixes #7927
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‎libcudacxx/benchmarks/bench/iota/basic.cu‎

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//===----------------------------------------------------------------------===//
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//
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// Part of CUDA Experimental in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#include <thrust/device_vector.h>
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#include <thrust/sequence.h>
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#include <cuda/memory_pool>
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#include <cuda/std/__pstl_algorithm>
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#include <cuda/stream>
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#include "nvbench_helper.cuh"
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template <typename T>
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static void basic(nvbench::state& state, nvbench::type_list<T>)
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{
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const auto elements = static_cast<std::size_t>(state.get_int64("Elements"));
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thrust::device_vector<T> in(elements, thrust::no_init);
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state.add_element_count(elements);
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state.add_global_memory_reads<T>(1);
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state.add_global_memory_writes<T>(elements);
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caching_allocator_t alloc{};
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state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync,
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[&](nvbench::launch& launch) {
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cuda::std::iota(cuda_policy(alloc, launch), in.begin(), in.end(), T{42});
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});
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}
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NVBENCH_BENCH_TYPES(basic, NVBENCH_TYPE_AXES(fundamental_types))
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.set_name("base")
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.set_type_axes_names({"T{ct}"})
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.add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4));
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template <typename T>
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static void stepped(nvbench::state& state, nvbench::type_list<T>)
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{
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const auto elements = static_cast<std::size_t>(state.get_int64("Elements"));
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thrust::device_vector<T> in(elements, thrust::no_init);
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state.add_element_count(elements);
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state.add_global_memory_reads<T>(1);
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state.add_global_memory_writes<T>(elements);
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caching_allocator_t alloc{};
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state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync,
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[&](nvbench::launch& launch) {
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cuda::std::iota(cuda_policy(alloc, launch), in.begin(), in.end(), T{42}, T{2});
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});
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}
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NVBENCH_BENCH_TYPES(stepped, NVBENCH_TYPE_AXES(fundamental_types))
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.set_name("stepped")
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.set_type_axes_names({"T{ct}"})
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.add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4));

‎libcudacxx/include/cuda/std/__pstl/iota.h‎

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//===----------------------------------------------------------------------===//
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//
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// Part of libcu++, the C++ Standard Library for your entire system,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#ifndef _CUDA_STD___PSTL_IOTA_H
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#define _CUDA_STD___PSTL_IOTA_H
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#include <cuda/std/detail/__config>
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#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
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# pragma GCC system_header
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#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
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# pragma clang system_header
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#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
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# pragma system_header
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#endif // no system header
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#if !_CCCL_COMPILER(NVRTC)
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# include <cuda/__iterator/counting_iterator.h>
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# include <cuda/__iterator/strided_iterator.h>
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# include <cuda/__nvtx/nvtx.h>
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# include <cuda/std/__concepts/concept_macros.h>
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# include <cuda/std/__execution/policy.h>
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# include <cuda/std/__functional/not_fn.h>
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# include <cuda/std/__iterator/concepts.h>
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# include <cuda/std/__iterator/incrementable_traits.h>
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# include <cuda/std/__iterator/iterator_traits.h>
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# include <cuda/std/__numeric/iota.h>
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# include <cuda/std/__pstl/dispatch.h>
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# include <cuda/std/__type_traits/always_false.h>
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# include <cuda/std/__type_traits/is_arithmetic.h>
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# include <cuda/std/__type_traits/is_convertible.h>
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# include <cuda/std/__type_traits/is_execution_policy.h>
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# include <cuda/std/__type_traits/is_integral.h>
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# include <cuda/std/__type_traits/is_nothrow_copy_constructible.h>
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# include <cuda/std/__type_traits/is_same.h>
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# include <cuda/std/__utility/move.h>
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# if _CCCL_HAS_BACKEND_CUDA()
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# include <cuda/std/__pstl/cuda/transform.h>
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# endif // _CCCL_HAS_BACKEND_CUDA()
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# include <cuda/std/__cccl/prologue.h>
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_CCCL_BEGIN_NAMESPACE_CUDA_STD
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template <class _Tp>
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_CCCL_CONCEPT __can_operator_plus_integral = _CCCL_REQUIRES_EXPR((_Tp), const _Tp& __val, const uint64_t __index)(
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requires(is_convertible_v<decltype(__val + __index), _Tp>));
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template <class _Tp>
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_CCCL_CONCEPT __can_operator_plus_conversion = _CCCL_REQUIRES_EXPR((_Tp), const _Tp& __val, const uint64_t __index)(
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requires(is_convertible_v<decltype(__val + static_cast<_Tp>(__index)), _Tp>));
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template <class _Tp>
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struct __iota_init_fn
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{
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_Tp __init_;
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_CCCL_API constexpr __iota_init_fn(const _Tp& __init) noexcept(is_nothrow_copy_constructible_v<_Tp>)
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: __init_(__init)
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{}
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[[nodiscard]] _CCCL_DEVICE_API _CCCL_FORCEINLINE _Tp constexpr operator()(const uint64_t __index) const
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{
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# if _CCCL_HAS_NVFP16()
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// We cannot rely on operator+ and constructors from integers to be available for the extended fp types
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if constexpr (is_same_v<_Tp, __half>)
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{
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return ::__hadd(__init_, ::__ull2half_rn(__index));
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}
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else
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# endif // _CCCL_HAS_NVFP16()
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# if _CCCL_HAS_NVBF16()
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if constexpr (is_same_v<_Tp, __nv_bfloat16>)
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{
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return ::__hadd(__init_, ::__ull2bfloat16_rn(__index));
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}
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else
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# endif // _CCCL_HAS_NVBF16()
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if constexpr (is_arithmetic_v<_Tp>)
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{ // avoid warnings about integer conversions
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return static_cast<_Tp>(__init_ + static_cast<_Tp>(__index));
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}
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else if constexpr (__can_operator_plus_integral<_Tp>)
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{
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return __init_ + __index;
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}
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else if constexpr (__can_operator_plus_conversion<_Tp>)
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{
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return __init_ + static_cast<_Tp>(__index);
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}
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else
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{
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static_assert(__always_false_v<_Tp>, "cuda::std::iota(iter, iter, init) requires that T supports operator+");
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}
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}
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};
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template <class _Tp>
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_CCCL_CONCEPT __can_operator_plus_times_integral = _CCCL_REQUIRES_EXPR((_Tp), const _Tp& __val, const uint64_t __index)(
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requires(is_convertible_v<decltype(__val + __val * __index), _Tp>));
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template <class _Tp>
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_CCCL_CONCEPT __can_operator_plus_times_conversion =
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_CCCL_REQUIRES_EXPR((_Tp), const _Tp& __val, const uint64_t __index) //
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(requires(is_convertible_v<decltype(__val + __val * static_cast<_Tp>(__index)), _Tp>));
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template <class _Tp>
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struct __iota_init_step_fn
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{
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_Tp __init_;
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_Tp __step_;
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_CCCL_API constexpr __iota_init_step_fn(const _Tp& __init,
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const _Tp& __step) noexcept(is_nothrow_copy_constructible_v<_Tp>)
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: __init_(__init)
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, __step_(__step)
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{}
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[[nodiscard]] _CCCL_DEVICE_API _CCCL_FORCEINLINE constexpr _Tp operator()(const uint64_t __index) const
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{
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# if _CCCL_HAS_NVFP16()
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// We cannot rely on operator+ and constructors from integers to be available for the extended fp types
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if constexpr (is_same_v<_Tp, __half>)
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{
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return ::__hadd(__init_, ::__hmul(__step_, ::__ull2half_rn(__index)));
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}
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else
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# endif // _CCCL_HAS_NVFP16()
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# if _CCCL_HAS_NVBF16()
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if constexpr (is_same_v<_Tp, __nv_bfloat16>)
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{
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return ::__hadd(__init_, ::__hmul(__step_, ::__ull2bfloat16_rn(__index)));
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}
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else
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# endif // _CCCL_HAS_NVBF16()
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if constexpr (is_arithmetic_v<_Tp>)
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{ // avoid warnings about integer conversions
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return static_cast<_Tp>(__init_ + __step_ * static_cast<_Tp>(__index));
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}
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else if constexpr (__can_operator_plus_times_integral<_Tp>)
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{
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return __init_ + __step_ * __index;
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}
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else if constexpr (__can_operator_plus_times_conversion<_Tp>)
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{
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return __init_ + __step_ * static_cast<_Tp>(__index);
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}
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else
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{
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static_assert(__always_false_v<_Tp>,
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"cuda::std::iota(iter, iter, init, step) requires that T supports operator+ and operator*");
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}
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}
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};
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_CCCL_BEGIN_NAMESPACE_ARCH_DEPENDENT
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_CCCL_TEMPLATE(class _Policy, class _InputIterator, class _Tp = iter_value_t<_InputIterator>)
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_CCCL_REQUIRES(__has_forward_traversal<_InputIterator> _CCCL_AND is_execution_policy_v<_Policy>)
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_CCCL_HOST_API void
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iota([[maybe_unused]] const _Policy& __policy, _InputIterator __first, _InputIterator __last, const _Tp& __init = _Tp{})
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{
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static_assert(indirectly_writable<_InputIterator, _Tp>,
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"cuda::std::iota requires InputIterator to be indirectly writable with T");
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[[maybe_unused]] auto __dispatch =
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::cuda::std::execution::__pstl_select_dispatch<::cuda::std::execution::__pstl_algorithm::__transform, _Policy>();
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if constexpr (::cuda::std::execution::__pstl_can_dispatch<decltype(__dispatch)>)
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{
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_CCCL_NVTX_RANGE_SCOPE("cuda::std::iota");
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if (__first == __last)
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{
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return;
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}
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// Note: using a different offset type than uint64_t degrades performance considerably for larger integer types
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const auto __count = static_cast<uint64_t>(::cuda::std::distance(__first, __last));
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// For whatever reason __iota_init_step_fn is much faster for int64_t and __int128
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if constexpr (is_arithmetic_v<_Tp>)
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{
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auto __res = __dispatch(
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__policy,
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::cuda::counting_iterator<uint64_t>{0},
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::cuda::counting_iterator<uint64_t>{__count},
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::cuda::std::move(__first),
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__iota_init_step_fn{__init, _Tp{1}});
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}
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else
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{
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auto __res = __dispatch(
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__policy,
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::cuda::counting_iterator<uint64_t>{0},
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::cuda::counting_iterator<uint64_t>{static_cast<uint64_t>(__count)},
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::cuda::std::move(__first),
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__iota_init_fn{__init});
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}
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}
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else
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{
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static_assert(__always_false_v<_Policy>, "Parallel cuda::std::iota requires at least one selected backend");
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return ::cuda::std::iota(::cuda::std::move(__first), ::cuda::std::move(__last), __init);
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}
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}
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_CCCL_TEMPLATE(class _Policy, class _InputIterator, class _Tp = iter_value_t<_InputIterator>)
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_CCCL_REQUIRES(__has_forward_traversal<_InputIterator> _CCCL_AND is_execution_policy_v<_Policy>)
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_CCCL_HOST_API void
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iota([[maybe_unused]] const _Policy& __policy,
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_InputIterator __first,
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_InputIterator __last,
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const _Tp& __init,
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const _Tp& __step)
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{
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static_assert(indirectly_writable<_InputIterator, _Tp>,
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"cuda::std::iota requires InputIterator to be indirectly writable with T");
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[[maybe_unused]] auto __dispatch =
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::cuda::std::execution::__pstl_select_dispatch<::cuda::std::execution::__pstl_algorithm::__transform, _Policy>();
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if constexpr (::cuda::std::execution::__pstl_can_dispatch<decltype(__dispatch)>)
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{
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_CCCL_NVTX_RANGE_SCOPE("cuda::std::iota");
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if (__first == __last)
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{
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return;
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}
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// Note: using a different offset type than uint64_t degrades performance considerably for larger integer types
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const auto __count = static_cast<uint64_t>(::cuda::std::distance(__first, __last));
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auto __res = __dispatch(
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__policy,
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::cuda::counting_iterator<uint64_t>{0},
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::cuda::counting_iterator<uint64_t>{__count},
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::cuda::std::move(__first),
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__iota_init_step_fn{__init, __step});
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}
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else
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{
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static_assert(__always_false_v<_Policy>, "Parallel cuda::std::iota requires at least one selected backend");
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// TODO(miscco): Consider adding that overload to serial iota
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return ::cuda::std::iota(::cuda::std::move(__first), ::cuda::std::move(__last), __init /*, __step*/);
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}
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}
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_CCCL_END_NAMESPACE_ARCH_DEPENDENT
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_CCCL_END_NAMESPACE_CUDA_STD
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# include <cuda/std/__cccl/epilogue.h>
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#endif // !_CCCL_COMPILER(NVRTC)
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#endif // _CUDA_STD___PSTL_IOTA_H

‎libcudacxx/include/cuda/std/__pstl_algorithm‎

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#include <cuda/std/__pstl/generate.h>
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#include <cuda/std/__pstl/generate_n.h>
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#include <cuda/std/__pstl/inclusive_scan.h>
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#include <cuda/std/__pstl/iota.h>
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#include <cuda/std/__pstl/merge.h>
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#include <cuda/std/__pstl/mismatch.h>
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#include <cuda/std/__pstl/none_of.h>

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