2a7ca101d7
Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream: Added: - python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc. Includes 204 .py files with full test coverage for all 27 algorithms - ci/ (163 files) — Build/test infrastructure build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml Directly maps to our [INFRA-CI] and [INFRA-BUILD] items - .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md - docs/ (491 files) — Official CCCL documentation CI references, CMake guides, Python compute docs, libcudacxx PTX docs - test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar) - Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml - CLAUDE.md symlink → AGENTS.md (NVIDIA's standard) cccl_upstream now mirrors full NVIDIA/cccl structure: Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks) After: 53M (+python +ci +docs +.agent +test +configs) This completes the CCCL base needed for: - [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds - [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations - [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh - Agent workflow: .agent/skills/ for consistent style and test patterns
84 lines
2.2 KiB
ReStructuredText
84 lines
2.2 KiB
ReStructuredText
.. _libcudacxx-extended-api-math-ilog:
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``cuda::ilog2``, ``cuda::ceil_ilog2``, ``cuda::ilog10``, and ``cuda::ceil_ilog10``
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=================================================================================
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Defined in the ``<cuda/cmath>`` header.
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.. code:: cuda
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namespace cuda {
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template <typename T>
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[[nodiscard]] __host__ __device__ __tile__ constexpr
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int ilog2(T value) noexcept;
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template <typename T>
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[[nodiscard]] __host__ __device__ __tile__ constexpr
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int ceil_ilog2(T value) noexcept;
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template <typename T>
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[[nodiscard]] __host__ __device__ __tile__ constexpr
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int ilog10(T value) noexcept;
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template <typename T>
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[[nodiscard]] __host__ __device__ __tile__ constexpr
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int ceil_ilog10(T value) noexcept;
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} // namespace cuda
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The functions compute the logarithm to the base 2 and 10 of an integer value.
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**Parameters**
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- ``value``: The input value.
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**Return value**
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- ``ilog2``, ``ceil_ilog2``: The logarithm to the base 2, rounded down and up to the nearest integer respectively.
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- ``ilog10``, ``ceil_ilog10``: The logarithm to the base 10, rounded down and up to the nearest integer respectively.
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**Constraints**
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- ``T`` is an integer type.
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**Preconditions**
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- ``value > 0``
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**Performance considerations**
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The functions perform the following operations in device code:
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- ``ilog2``: ``FLO``
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- ``ceil_ilog2``: ``FLO``, ``POPC``, ``ADD``, comparison
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- ``ilog10``: ``FLO``, ``FMUL``, ``F2I``, constant memory lookup, ``SEL`` + ``IADD`` only if ``T == uint32_t`` or ``T == __uint128_t``
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- ``ceil_ilog10``: ``ilog10`` with an additional comparison, subtraction, and ``IADD``
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Example
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-------
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.. code:: cuda
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#include <cuda/cmath>
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#include <cuda/std/cassert>
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__global__ void ilog_kernel() {
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assert(cuda::ilog2(20) == 4);
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assert(cuda::ceil_ilog2(20) == 5);
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assert(cuda::ilog2(32) == 5);
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assert(cuda::ceil_ilog2(32) == 5);
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assert(cuda::ilog10(100) == 2);
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assert(cuda::ilog10(2000) == 3);
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assert(cuda::ceil_ilog10(100) == 2);
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assert(cuda::ceil_ilog10(2000) == 4);
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}
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int main() {
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ilog_kernel<<<1, 1>>>();
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cudaDeviceSynchronize();
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return 0;
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}
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`See it on Godbolt 🔗 <https://godbolt.org/z/7W3WaGd3c>`__
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