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
33 lines
1.7 KiB
Markdown
33 lines
1.7 KiB
Markdown
## CUDA Experimental: Library for experimental features in CUDA Core Compute Libraries.
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CUDA Experimental serves as a distribution channel for features that are considered experimental in the CUDA Core Compute Libraries.
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Some of them are still actively designed or developed and their API is evolving.
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Some of them are specific to one hardware architecture and are still looking for a generic and forward compatible exposure.
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Finally, some of them need to prove useful enough to deserve long term support.
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**All APIs available in CUDA Experimental are not considered stable and can change without a notice.** They can also be deprecated or removed on a much faster cadence than in other CCCL libraries.
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Features are exposed here for the CUDA C++ community to experiment with and provide feedback on how to shape it to best fit their use cases.
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Once we become confident a feature is ready and would be a great permanent addition in CCCL, it will become a part of some other CCCL library with a stable API.
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## Installation
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CUDA Experimental library is **not** distributed with the CUDA Toolkit like the rest of CCCL. It is only available on the [CCCL GitHub repository](https://github.com/NVIDIA/cccl).
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CUDA Experimental compilation requires C++17 standard or newer. Supported compilers are:
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CUDA Compilers:
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- NVCC 12.3+
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NVCC host compilers:
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- GCC 7+
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- Clang 9+
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- MSVC 2019+
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Everything in CUDA Experimental is header-only, so cloning and including it in a simple project is as easy as the following:
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```bash
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git clone https://github.com/NVIDIA/cccl.git
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# Note:
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nvcc -Icccl/cudax/include main.cu -o main
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```
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A CMake target `cudax::cudax` is available as part of the CCCL package when `CCCL_ENABLE_UNSTABLE` is set to a truthy value before calling `find_package` or `add_subdirectory`.
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