dedf08166a
Added 863 files from NVIDIA/cccl sparse checkout: - c2h/ (27 files): Catch2 test helpers — generators, validators, runner - nvbench_helper/ (10 files): Benchmark harness utilities - cmake/ (29 files): CMake presets and build helpers - cudax/ (794 files): Experimental CUDA extensions - AGENTS.md: NVIDIA's official AI agent instructions for CCCL - CMakePresets.json: Standardized build configurations - cccl-version.json: Version tracking Also added CCCL_ASSET_MAP.md mapping all 4295 CCCL files to competition value and PRD items. cccl_upstream now covers 100% of competition-critical assets: - 27 tuning headers (SM80/90/100 benchmark data) - 32 dispatch headers (algorithm implementations) - 60 Thrust examples (correctness verification) - 217 CUB Catch2 tests (regression matrix) - 153 CUB benchmarks (parameter space search) - 18 CUB examples (API verification) - 27 test helpers + benchmark harness - 794 cudax experimental extensions
CUDA Experimental: Library for experimental features in CUDA Core Compute Libraries.
CUDA Experimental serves as a distribution channel for features that are considered experimental in the CUDA Core Compute Libraries. Some of them are still actively designed or developed and their API is evolving. Some of them are specific to one hardware architecture and are still looking for a generic and forward compatible exposure. Finally, some of them need to prove useful enough to deserve long term support.
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.
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. 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.
Installation
CUDA Experimental library is not distributed with the CUDA Toolkit like the rest of CCCL. It is only available on the CCCL GitHub repository.
CUDA Experimental compilation requires C++17 standard or newer. Supported compilers are:
CUDA Compilers:
- NVCC 12.3+
NVCC host compilers:
- GCC 7+
- Clang 9+
- MSVC 2019+
Everything in CUDA Experimental is header-only, so cloning and including it in a simple project is as easy as the following:
git clone https://github.com/NVIDIA/cccl.git
# Note:
nvcc -Icccl/cudax/include main.cu -o main
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.