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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
This directory contains examples of how to use CCCL in your project.
See the README.md in each subdirectory for more information.
To build and run only the examples, run the following commands from the root directory of the repository:
cmake -S . -B build -DCCCL_ENABLE_EXAMPLES=ON -DCCCL_ENABLE_THRUST=OFF -DCCCL_ENABLE_CUB=OFF -DCCCL_ENABLE_LIBCUDACXX=OFF -DCCCL_ENABLE_TESTING=OFF
cmake --build build
ctest --test-dir build --output-on-failure