project_6_89d52222/cccl_upstream/examples/README.md


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:

```bash
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
```
[INFRA] Import NVIDIA/CCCL upstream as optimization reference library 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 2026-07-30 09:35:51 +00:00
			`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:`

			```bash
			`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`
			```