CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
142 lines
5.1 KiB
Markdown
142 lines
5.1 KiB
Markdown
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# Example Project Using CCCL From GitHub
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Many CUDA C++ users are accustomed to using CCCL headers (Thrust, CUB, libcu++) provided with the [NVIDIA CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit) or [NVIDIA HPC SDK](https://developer.nvidia.com/hpc-sdk).
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In addition, we also support using CCCL directly from GitHub.
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The primary benefit is that this allows users to use the latest version of CCCL without having to wait for a new release of the CUDA Toolkit or HPC SDK.
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This example demonstrates how to use CCCL from GitHub in a CMake project.
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## Overview
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This is a standalone example of how to use [CCCL](https://github.com/nvidia/cccl) in a CMake project.
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This example demonstrates fetching CCCL from GitHub and linking it with a simple example CUDA program ([`example.cu`](example.cu)) that utilizes the headers from CCCL.
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This is intended to be a starting point for users who want to use CCCL in their own projects.
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## How to Adapt This Example to Your Project
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This example is intended to be a starting point for users who want to use CCCL in their own projects.
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In order to adapt this example to your project, you will need to do the following:
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1. Download `CPM.cmake` into your project's `cmake/` directory ([see below for instructions](#downloading-cpm)).
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2. Add the following lines to your project's `CMakeLists.txt` file:
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```cmake
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include(cmake/CPM.cmake)
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# This will automatically clone CCCL from GitHub and make the exported cmake targets available
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CPMAddPackage(
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NAME CCCL
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GITHUB_REPOSITORY nvidia/cccl
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GIT_TAG main # Fetches the latest commit on the main branch
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)
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# If you're building an executable
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add_executable(your_executable your_file.cu)
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target_link_libraries(your_executable PRIVATE CCCL::CCCL)
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# Alternatively, if you're building a library
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add_library(your_library SHARED your_file.cu)
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target_link_libraries(your_library PRIVATE CCCL::CCCL)
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```
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See the [CMakeLists.txt](CMakeLists.txt) file in this directory for a complete example.
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3. Configure and build your project as normal and verify that it builds successfully.
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For more information on using CPM, see [below](#using-cmake-package-manager).
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## Using CMake Package Manager
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This example uses the CMake Package Manager (CPM) to fetch CCCL from GitHub.
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See the [CMakeLists.txt](CMakeLists.txt) file in this directory for the complete example.
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If you are not familiar with CPM, you can find more information [here](https://github.com/cpm-cmake/CPM.cmake).
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In short, CPM is a CMake module that simplifies dependency management for CMake projects.
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It automatically downloads and integrates dependencies into your CMake project.
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### Downloading CPM
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In order to get the latest version of CPM.cmake, you can run the following command in the root directory of your project:
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```bash
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mkdir -p cmake
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wget -O cmake/CPM.cmake https://github.com/cpm-cmake/CPM.cmake/releases/latest/download/get_cpm.cmake
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```
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This will download and create the file `cmake/CPM.cmake` in your project directory.
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Most projects will want to commit this file to their source control system.
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You can then use `include(cmake/CPM.cmake)` in your project's `CMakeLists.txt` file to include CPM in your project.
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Alternatively, you can add the following logic to your `CMakeLists.txt` to download CPM if it is not already present in your project directory.
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```cmake
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set(CPM_DOWNLOAD_VERSION 0.34.0)
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if(CPM_SOURCE_CACHE)
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set(CPM_DOWNLOAD_LOCATION "${CPM_SOURCE_CACHE}/cpm/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
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elseif(DEFINED ENV{CPM_SOURCE_CACHE})
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set(CPM_DOWNLOAD_LOCATION "$ENV{CPM_SOURCE_CACHE}/cpm/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
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else()
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set(CPM_DOWNLOAD_LOCATION "${CMAKE_BINARY_DIR}/cmake/CPM_${CPM_DOWNLOAD_VERSION}.cmake")
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endif()
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if(NOT (EXISTS ${CPM_DOWNLOAD_LOCATION}))
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message(STATUS "Downloading CPM.cmake to ${CPM_DOWNLOAD_LOCATION}")
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file(DOWNLOAD
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https://github.com/TheLartians/CPM.cmake/releases/download/v${CPM_DOWNLOAD_VERSION}/CPM.cmake
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${CPM_DOWNLOAD_LOCATION}
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)
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endif()
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include(${CPM_DOWNLOAD_LOCATION})
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```
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## Building and Running the Example
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Most people will want to adapt this example to their own project as described [above](#how-to-adapt-this-example-to-your-project). If you would like to build and run this example as-is, you will need to follow the instructions below.
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### Prerequisites
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If you would like to build and run this example as-is, you will need:
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- A CUDA-capable GPU
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- NVIDIA CUDA Toolkit (12 or later)
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- CMake (3.14 or later)
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- A C++17 standard-compliant compiler
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- git
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### Instructions
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1. Clone this repository to your local machine.
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```bash
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git clone https://github.com/NVIDIA/cccl.git
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```
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2. Enter the directory of the cloned repository.
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```bash
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cd cccl/examples/example_project
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```
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3. Run the CMake configure step
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```bash
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cmake -S . -B build
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```
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Alternatively,
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```bash
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mkdir -p build
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cd build
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cmake ..
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```
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4. Run the CMake build step.
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```bash
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cmake --build .
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```
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6. Run the executable.
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```bash
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./build/example_project
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```
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If everything is configured correctly, the program will execute and print the sum of an array of integers, demonstrating the use of cccl.
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