project_6/cccl_upstream/thrust/examples/README.md

Once Thrust has been installed, these example programs can be compiled
directly with nvcc.  For example, the following command will compile the
`norm` example.

```bash
$ nvcc norm.cu -o norm
```

These examples are also available online:
https://github.com/NVIDIA/cccl/tree/main/thrust/examples

For any serious experimentation, we recommend using CMake and [CCCL from GitHub](https://github.com/NVIDIA/cccl).
We also provide consistent and convenient development environments as [devcontainers](../../.devcontainers/README.md).
[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			`Once Thrust has been installed, these example programs can be compiled`
			`directly with nvcc. For example, the following command will compile the`
			`norm` example.

			```bash
			`$ nvcc norm.cu -o norm`
			```

			`These examples are also available online:`
			`https://github.com/NVIDIA/cccl/tree/main/thrust/examples`

			`For any serious experimentation, we recommend using CMake and [CCCL from GitHub](https://github.com/NVIDIA/cccl).`
			`We also provide consistent and convenient development environments as [devcontainers](../../.devcontainers/README.md).`