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
115 lines
3.6 KiB
ReStructuredText
115 lines
3.6 KiB
ReStructuredText
.. _thrust-module:
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Thrust
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======
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.. toctree::
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:hidden:
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:maxdepth: 2
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Overview <self>
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algorithms
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containers
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function_objects
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iterators
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memory_management
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numerics
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parallel_execution_policies
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random
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system
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utility
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API reference <api/index>
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developer_overview
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Thrust is the C++ parallel algorithms library which inspired the introduction of parallel algorithms to the
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C++ Standard Library. Thrust's **high-level** interface greatly enhances programmer **productivity** while
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enabling performance portability between GPUs and multicore CPUs.
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It builds on top of established parallel programming frameworks (such as CUDA, TBB, and OpenMP).
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It also provides a number of general-purpose facilities similar to those found in the C++ Standard Library.
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Thrust is an open source project; it is available on `GitHub <https://github.com/NVIDIA/cccl>`__
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and included in the NVIDIA HPC SDK and CUDA Toolkit.
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If you have one of those SDKs installed, no additional installation or compiler flags are needed to use Thrust.
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Examples
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--------
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Thrust is best learned through examples.
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-------------
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CMake Example
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-------------
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A complete, standalone example project showing how to write a CMake build system that uses Thrust with any supported
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device system is available in the CCCL repository `here <https://github.com/NVIDIA/cccl/tree/main/examples/thrust_flexible_device_system>`__.
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------------------
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Thrust API Example
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------------------
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The following example generates random numbers serially and then transfers them
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to a parallel device where they are sorted.
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.. code:: cpp
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#include <thrust/host_vector.h>
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#include <thrust/device_vector.h>
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#include <thrust/generate.h>
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#include <thrust/sort.h>
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#include <thrust/copy.h>
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#include <thrust/random.h>
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int main() {
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// Generate 32M random numbers serially.
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thrust::default_random_engine rng(1337);
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thrust::uniform_int_distribution<int> dist;
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thrust::host_vector<int> h_vec(32 << 20);
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thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });
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// Transfer data to the device.
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thrust::device_vector<int> d_vec = h_vec;
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// Sort data on the device.
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thrust::sort(d_vec.begin(), d_vec.end());
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// Transfer data back to host.
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thrust::copy(d_vec.begin(), d_vec.end(), h_vec.begin());
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}
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`See it on Godbolt <https://godbolt.org/z/GeWEd8Er9>`__
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This example demonstrates computing the sum of some random numbers in parallel:
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.. code:: cpp
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#include <thrust/host_vector.h>
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#include <thrust/device_vector.h>
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#include <thrust/generate.h>
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#include <thrust/reduce.h>
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#include <thrust/functional.h>
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#include <thrust/random.h>
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int main() {
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// Generate random data serially.
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thrust::default_random_engine rng(1337);
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thrust::uniform_real_distribution<double> dist(-50.0, 50.0);
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thrust::host_vector<double> h_vec(32 << 20);
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thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });
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// Transfer to device and compute the sum.
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thrust::device_vector<double> d_vec = h_vec;
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double x = thrust::reduce(d_vec.begin(), d_vec.end(), 0, thrust::plus<int>());
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}
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`See it on Godbolt <https://godbolt.org/z/cnsbWWME7>`__
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Getting The Thrust Source Code & Developing Thrust
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-----------------------------------------------------
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Thrust started as a stand-alone project, but as of March 2024 Thrust is a part of the
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CUDA Core Compute Libraries (CCCL). Please refer to the
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`CCCL Getting Started section <https://github.com/NVIDIA/cccl?tab=readme-ov-file#getting-started>`__ and the
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`Contributing Guide: <https://github.com/NVIDIA/cccl/blob/main/CONTRIBUTING.md>`__ for instructions on how to
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get started developing the CCCL sources.
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