56fd68e7dd
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
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465 B
Markdown
13 lines
465 B
Markdown
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This directory contains examples of how to use CCCL in your project.
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See the `README.md` in each subdirectory for more information.
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To build and run only the examples, run the following commands from the root directory of the repository:
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```bash
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cmake -S . -B build -DCCCL_ENABLE_EXAMPLES=ON -DCCCL_ENABLE_THRUST=OFF -DCCCL_ENABLE_CUB=OFF -DCCCL_ENABLE_LIBCUDACXX=OFF -DCCCL_ENABLE_TESTING=OFF
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cmake --build build
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ctest --test-dir build --output-on-failure
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```
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